{"as_of":"2026-08-12T23:29:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:d66acf137e894644e96aecdbf8bf3677b367e86c4aff6fdea3d6fc9d33f99068","coverage":[{"denominator":65,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":65,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-12T12:27:11.604333Z","state":"measured"},{"denominator":65,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":65,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-12T06:34:41.77262+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2411.17214/citation-record","integrity":"/paper/2411.17214/integrity","json":"/paper/2411.17214/citation-record.json","paper":"/paper/2411.17214"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:27:12.417922Z","title":"Reconstructing an image from its local descriptors,","venue":null,"work_id":"dd76fd86-318c-4586-b2cc-080603372cd9","year":2011},"citing_paper":{"arxiv_id":"2411.17214","last_updated":"2025-03-19T03:09:55Z","snapshot_observed_at":"2026-08-12T12:20:27.476209Z","submitted_at":"2024-11-26T08:30:31Z","title":"MAT: Multi-Range Attention Transformer for Efficient Image Super-Resolution","version":3},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:11.354413Z"},"links":{"citing_paper":"/paper/2411.17214"},"observation_digest":"sha256:0f96a3b0953902f7c5e734539383b631009f9931dabc37224da797fe033c3356","observation_id":"56044b14-abfa-432d-8d72-55cfd38864e9","resolution":{"observed_at":"2026-08-12T12:27:12.421976Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:27:12.405411Z","title":"Boosting single image super-resolution via partial channel shifting,","venue":null,"work_id":"8ff3a410-da15-44bb-a92a-019324ae7647","year":2023},"citing_paper":{"arxiv_id":"2411.17214","last_updated":"2025-03-19T03:09:55Z","snapshot_observed_at":"2026-08-12T12:20:27.476209Z","submitted_at":"2024-11-26T08:30:31Z","title":"MAT: Multi-Range Attention Transformer for Efficient Image Super-Resolution","version":3},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:11.358755Z"},"links":{"citing_paper":"/paper/2411.17214"},"observation_digest":"sha256:274f8c795ccb4583f28ef63d2ebb93a745c7858f74000ff191f518af892f1fdd","observation_id":"7126452c-9536-4fbd-b284-071d20715bbd","resolution":{"observed_at":"2026-08-12T12:27:12.409837Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:27:12.390265Z","title":"Matching local self-similarities across images and videos,","venue":null,"work_id":"0d9277f9-5d42-44d4-a669-ee846abc47e4","year":2007},"citing_paper":{"arxiv_id":"2411.17214","last_updated":"2025-03-19T03:09:55Z","snapshot_observed_at":"2026-08-12T12:20:27.476209Z","submitted_at":"2024-11-26T08:30:31Z","title":"MAT: Multi-Range Attention Transformer for Efficient Image Super-Resolution","version":3},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:11.362433Z"},"links":{"citing_paper":"/paper/2411.17214"},"observation_digest":"sha256:f0e05544954bd921f4676dbcbdb8a0fb1e8246072c937a780dbe3c2e63c20dcc","observation_id":"cdd8302d-52c9-442e-93b6-2ad184448250","resolution":{"observed_at":"2026-08-12T12:27:12.395464Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:27:12.373009Z","title":"Super-resolution from a single image,","venue":null,"work_id":"029b1b1c-a0fe-40fd-b42d-a309225bf9b1","year":2009},"citing_paper":{"arxiv_id":"2411.17214","last_updated":"2025-03-19T03:09:55Z","snapshot_observed_at":"2026-08-12T12:20:27.476209Z","submitted_at":"2024-11-26T08:30:31Z","title":"MAT: Multi-Range Attention Transformer for Efficient Image Super-Resolution","version":3},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:11.366299Z"},"links":{"citing_paper":"/paper/2411.17214"},"observation_digest":"sha256:ba080e917303a8dd3ee5ef9e408978f60025ca59fc85209064a72d46a7e96f1d","observation_id":"67bdd80a-0f86-4ce2-84a3-2433654c543b","resolution":{"observed_at":"2026-08-12T12:27:12.377758Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:27:12.357185Z","title":"Image super-resolution with self- similarity prior guided network and sample-discriminating learning,","venue":null,"work_id":"632b879e-67f2-43b9-bba5-4603b7b0566e","year":1966},"citing_paper":{"arxiv_id":"2411.17214","last_updated":"2025-03-19T03:09:55Z","snapshot_observed_at":"2026-08-12T12:20:27.476209Z","submitted_at":"2024-11-26T08:30:31Z","title":"MAT: Multi-Range Attention Transformer for Efficient Image Super-Resolution","version":3},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:11.370360Z"},"links":{"citing_paper":"/paper/2411.17214"},"observation_digest":"sha256:16d418341c77eb6f95eeb3cdc5f0285baac5b81971f32d88dd6ce98b54baae75","observation_id":"9f7a22d6-d2d6-4f03-824d-ad6acf7f264f","resolution":{"observed_at":"2026-08-12T12:27:12.362812Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:27:12.342947Z","title":"Single image super-resolution from transformed self-exemplars,","venue":null,"work_id":"433d5a71-1573-41fd-8187-3cba6d092de6","year":2015},"citing_paper":{"arxiv_id":"2411.17214","last_updated":"2025-03-19T03:09:55Z","snapshot_observed_at":"2026-08-12T12:20:27.476209Z","submitted_at":"2024-11-26T08:30:31Z","title":"MAT: Multi-Range Attention Transformer for Efficient Image Super-Resolution","version":3},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:11.374249Z"},"links":{"citing_paper":"/paper/2411.17214"},"observation_digest":"sha256:f3667ebc75bc3ec16f3ee2e9b7d25006c9d8e11d21037865210e83e69a88d7b6","observation_id":"ea87561e-5f4a-4adc-a0a8-112a246aecba","resolution":{"observed_at":"2026-08-12T12:27:12.347750Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:27:12.329747Z","title":"Image super-resolution using deep convolutional networks,","venue":null,"work_id":"28a77ab3-1e9b-47ca-9ee0-3b7edb2f8c4c","year":null},"citing_paper":{"arxiv_id":"2411.17214","last_updated":"2025-03-19T03:09:55Z","snapshot_observed_at":"2026-08-12T12:20:27.476209Z","submitted_at":"2024-11-26T08:30:31Z","title":"MAT: Multi-Range Attention Transformer for Efficient Image Super-Resolution","version":3},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:11.378348Z"},"links":{"citing_paper":"/paper/2411.17214"},"observation_digest":"sha256:bf75979cb84b65d17794c53df2c86462d312afcda18a394e4464263fb91c9ec2","observation_id":"c54d1d60-6874-4ca8-ad85-a0ae1cf42122","resolution":{"observed_at":"2026-08-12T12:27:12.334266Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:27:12.314076Z","title":"Accurate image super-resolution using very deep convolutional networks,","venue":null,"work_id":"b2a046b5-a8b2-4ea4-8570-f9fff6bc48bb","year":2016},"citing_paper":{"arxiv_id":"2411.17214","last_updated":"2025-03-19T03:09:55Z","snapshot_observed_at":"2026-08-12T12:20:27.476209Z","submitted_at":"2024-11-26T08:30:31Z","title":"MAT: Multi-Range Attention Transformer for Efficient Image Super-Resolution","version":3},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:11.382069Z"},"links":{"citing_paper":"/paper/2411.17214"},"observation_digest":"sha256:95c3f7087a818e17be708b52f3faed4c7469f0b67e6b5ff50ace89ee745dfbdc","observation_id":"91eed41f-46a1-4ee4-b900-a5ef9dd38283","resolution":{"observed_at":"2026-08-12T12:27:12.319786Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:27:12.301916Z","title":"Photo-realistic single im- age super-resolution using a generative adversarial network,","venue":null,"work_id":"1f93d365-7cb6-4965-9360-8775336650f8","year":2017},"citing_paper":{"arxiv_id":"2411.17214","last_updated":"2025-03-19T03:09:55Z","snapshot_observed_at":"2026-08-12T12:20:27.476209Z","submitted_at":"2024-11-26T08:30:31Z","title":"MAT: Multi-Range Attention Transformer for Efficient Image Super-Resolution","version":3},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:11.385679Z"},"links":{"citing_paper":"/paper/2411.17214"},"observation_digest":"sha256:f047f9efda69ad4708eb1e42c798156a20d379c47f08d5c197facd5adb236ada","observation_id":"34d221ad-7e88-4c00-b91f-20fdef82569f","resolution":{"observed_at":"2026-08-12T12:27:12.305930Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:27:12.290315Z","title":"Enhanced deep resid- ual networks for single image super-resolution,","venue":null,"work_id":"a8cf17c1-e02c-4389-b20d-7201d3844079","year":2017},"citing_paper":{"arxiv_id":"2411.17214","last_updated":"2025-03-19T03:09:55Z","snapshot_observed_at":"2026-08-12T12:20:27.476209Z","submitted_at":"2024-11-26T08:30:31Z","title":"MAT: Multi-Range Attention Transformer for Efficient Image Super-Resolution","version":3},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:11.389254Z"},"links":{"citing_paper":"/paper/2411.17214"},"observation_digest":"sha256:7795681e0f6b7432ed2e7b055891e52831565fef816da3334a83155196986312","observation_id":"c26df218-2f9f-4725-bce2-013f0f9e7a6d","resolution":{"observed_at":"2026-08-12T12:27:12.294600Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:27:12.278517Z","title":"Residual dense network for image super-resolution,","venue":null,"work_id":"e0ac63f9-fd6d-4325-9681-129694eeec7b","year":2018},"citing_paper":{"arxiv_id":"2411.17214","last_updated":"2025-03-19T03:09:55Z","snapshot_observed_at":"2026-08-12T12:20:27.476209Z","submitted_at":"2024-11-26T08:30:31Z","title":"MAT: Multi-Range Attention Transformer for Efficient Image Super-Resolution","version":3},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:11.392741Z"},"links":{"citing_paper":"/paper/2411.17214"},"observation_digest":"sha256:a2f21b49769bc10880ec1a422dc819e63d86acc54ed4242e8eb2b7003db470e8","observation_id":"3c24aca2-f3ad-4456-9bcb-d1c6a90d85bc","resolution":{"observed_at":"2026-08-12T12:27:12.282470Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:27:12.266902Z","title":"Multi-grained attention networks for single image super-resolution,","venue":null,"work_id":"eeb3d197-8408-4083-befe-f62ee0ef76e6","year":2021},"citing_paper":{"arxiv_id":"2411.17214","last_updated":"2025-03-19T03:09:55Z","snapshot_observed_at":"2026-08-12T12:20:27.476209Z","submitted_at":"2024-11-26T08:30:31Z","title":"MAT: Multi-Range Attention Transformer for Efficient Image Super-Resolution","version":3},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:11.396261Z"},"links":{"citing_paper":"/paper/2411.17214"},"observation_digest":"sha256:00577c72ff8685a5490efccf01c769d4cf4b8243a7ed745c97ce501c1a58e647","observation_id":"08afa332-62a7-41c3-b45f-8bd4b14a13ce","resolution":{"observed_at":"2026-08-12T12:27:12.270957Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:27:12.254373Z","title":"Lightweight image super- resolution with information multi-distillation network,","venue":null,"work_id":"b5aa64cf-7368-4416-a5e2-117701e265d1","year":2019},"citing_paper":{"arxiv_id":"2411.17214","last_updated":"2025-03-19T03:09:55Z","snapshot_observed_at":"2026-08-12T12:20:27.476209Z","submitted_at":"2024-11-26T08:30:31Z","title":"MAT: Multi-Range Attention Transformer for Efficient Image Super-Resolution","version":3},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:11.401254Z"},"links":{"citing_paper":"/paper/2411.17214"},"observation_digest":"sha256:1aefceb6449b1566854692c4059d3f38c98caf55582f7b9eff721d356e1b994f","observation_id":"6b296e38-d368-495f-a028-550fa09dbee5","resolution":{"observed_at":"2026-08-12T12:27:12.258515Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:27:12.242515Z","title":"Blueprint separable residual network for efficient image super-resolution,","venue":null,"work_id":"a7f06187-c1f0-440f-9366-5e842cb4cbba","year":2022},"citing_paper":{"arxiv_id":"2411.17214","last_updated":"2025-03-19T03:09:55Z","snapshot_observed_at":"2026-08-12T12:20:27.476209Z","submitted_at":"2024-11-26T08:30:31Z","title":"MAT: Multi-Range Attention Transformer for Efficient Image Super-Resolution","version":3},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:11.405721Z"},"links":{"citing_paper":"/paper/2411.17214"},"observation_digest":"sha256:51680bb2b4adde9a61a0d0894dc53d29f89d475e7edc072377d710f310ff389b","observation_id":"ef5d9a14-56c5-4464-a29b-2348cd1d87da","resolution":{"observed_at":"2026-08-12T12:27:12.246471Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:27:12.230324Z","title":"Large kernel distillation network for efficient single image super-resolution,","venue":null,"work_id":"e414ca85-7760-4e36-881a-56c5d3811de4","year":2023},"citing_paper":{"arxiv_id":"2411.17214","last_updated":"2025-03-19T03:09:55Z","snapshot_observed_at":"2026-08-12T12:20:27.476209Z","submitted_at":"2024-11-26T08:30:31Z","title":"MAT: Multi-Range Attention Transformer for Efficient Image Super-Resolution","version":3},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:11.409553Z"},"links":{"citing_paper":"/paper/2411.17214"},"observation_digest":"sha256:e6858b8b4f96ec336a982b86f9976d1ac6228b38b12d78d5f1c80032a428c792","observation_id":"1f258a1e-fb29-4e40-ae3e-cb8833eea0a4","resolution":{"observed_at":"2026-08-12T12:27:12.234376Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:27:12.218818Z","title":"Image super- resolution using very deep residual channel attention networks,","venue":null,"work_id":"e4dc15cb-b433-4ab8-b55b-6e59057b86dd","year":2018},"citing_paper":{"arxiv_id":"2411.17214","last_updated":"2025-03-19T03:09:55Z","snapshot_observed_at":"2026-08-12T12:20:27.476209Z","submitted_at":"2024-11-26T08:30:31Z","title":"MAT: Multi-Range Attention Transformer for Efficient Image Super-Resolution","version":3},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:11.413096Z"},"links":{"citing_paper":"/paper/2411.17214"},"observation_digest":"sha256:59e46705e55a45b402750449c4db08ca512f906234efc1d2ed41db8e59a1125a","observation_id":"31a1afa3-828d-4ff9-bbdc-b2f3b98d77ec","resolution":{"observed_at":"2026-08-12T12:27:12.222669Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:27:12.201907Z","title":"Residual feature aggregation network for image super-resolution,","venue":null,"work_id":"b4d7afe8-bb4d-4d5c-b6b4-f2130eb434ba","year":2020},"citing_paper":{"arxiv_id":"2411.17214","last_updated":"2025-03-19T03:09:55Z","snapshot_observed_at":"2026-08-12T12:20:27.476209Z","submitted_at":"2024-11-26T08:30:31Z","title":"MAT: Multi-Range Attention Transformer for Efficient Image Super-Resolution","version":3},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:11.417368Z"},"links":{"citing_paper":"/paper/2411.17214"},"observation_digest":"sha256:bdef92184d46d615dc0b2e164c3f39963e33b6ee7eef1368b9783e6d2c19f462","observation_id":"0157544f-74bd-4b7d-9094-dde19d0fa045","resolution":{"observed_at":"2026-08-12T12:27:12.206117Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:27:12.189032Z","title":"Image super-resolution with non-local sparse attention,","venue":null,"work_id":"eb11b6f5-bdd6-4197-8232-abf3a551f6b0","year":2021},"citing_paper":{"arxiv_id":"2411.17214","last_updated":"2025-03-19T03:09:55Z","snapshot_observed_at":"2026-08-12T12:20:27.476209Z","submitted_at":"2024-11-26T08:30:31Z","title":"MAT: Multi-Range Attention Transformer for Efficient Image Super-Resolution","version":3},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:11.421108Z"},"links":{"citing_paper":"/paper/2411.17214"},"observation_digest":"sha256:bc11b14cfc4f684f67ce34713e08cb2bb991f46be95c7218307cf0b84d37eeae","observation_id":"179c5162-596d-4b8c-a195-d003a6be629c","resolution":{"observed_at":"2026-08-12T12:27:12.193873Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:27:12.177557Z","title":"Attention is all you need,","venue":null,"work_id":"d3e28f8f-abf7-4b95-b719-7aabc32a3959","year":2017},"citing_paper":{"arxiv_id":"2411.17214","last_updated":"2025-03-19T03:09:55Z","snapshot_observed_at":"2026-08-12T12:20:27.476209Z","submitted_at":"2024-11-26T08:30:31Z","title":"MAT: Multi-Range Attention Transformer for Efficient Image Super-Resolution","version":3},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:11.425422Z"},"links":{"citing_paper":"/paper/2411.17214"},"observation_digest":"sha256:4cdb997aaa7510aec1c458a9f4981231a2c44ca1e11765481c2011831bc0dd7f","observation_id":"ebeb9c25-4147-40c2-9cdb-412d55a88674","resolution":{"observed_at":"2026-08-12T12:27:12.181753Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:27:12.166245Z","title":"Interpreting super-resolution networks with local attribution maps,","venue":null,"work_id":"07fb7340-c565-4387-900e-a47ed111ef4e","year":2021},"citing_paper":{"arxiv_id":"2411.17214","last_updated":"2025-03-19T03:09:55Z","snapshot_observed_at":"2026-08-12T12:20:27.476209Z","submitted_at":"2024-11-26T08:30:31Z","title":"MAT: Multi-Range Attention Transformer for Efficient Image Super-Resolution","version":3},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:11.429427Z"},"links":{"citing_paper":"/paper/2411.17214"},"observation_digest":"sha256:d728dabca0a742d866113904373b4d1d9f224cea1e124c6f03f3719772acdb4e","observation_id":"7ae6c25d-6533-48e8-977e-28928b30e8dc","resolution":{"observed_at":"2026-08-12T12:27:12.169970Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:27:12.155167Z","title":"Swinir: Image restoration using swin transformer,","venue":null,"work_id":"5d633d12-1063-4d8e-81fa-794d25a78074","year":2021},"citing_paper":{"arxiv_id":"2411.17214","last_updated":"2025-03-19T03:09:55Z","snapshot_observed_at":"2026-08-12T12:20:27.476209Z","submitted_at":"2024-11-26T08:30:31Z","title":"MAT: Multi-Range Attention Transformer for Efficient Image Super-Resolution","version":3},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:11.433261Z"},"links":{"citing_paper":"/paper/2411.17214"},"observation_digest":"sha256:67652003002a483155030e984cfd17e701e906ee4df54179bde8c0c189034a6a","observation_id":"48d8c51a-15aa-431f-a2c8-13fcf0d15298","resolution":{"observed_at":"2026-08-12T12:27:12.159032Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:27:12.143054Z","title":"Activating more pixels in image super-resolution transformer,","venue":null,"work_id":"0e219130-4500-4cfc-9702-9c731a60fb4a","year":2023},"citing_paper":{"arxiv_id":"2411.17214","last_updated":"2025-03-19T03:09:55Z","snapshot_observed_at":"2026-08-12T12:20:27.476209Z","submitted_at":"2024-11-26T08:30:31Z","title":"MAT: Multi-Range Attention Transformer for Efficient Image Super-Resolution","version":3},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:11.436910Z"},"links":{"citing_paper":"/paper/2411.17214"},"observation_digest":"sha256:8c90e37dfe132521401292d4913d7a54436a8f83139e69a48ca0389603597716","observation_id":"bc3c4df8-3359-4c32-b4e4-ab7dc809fc4c","resolution":{"observed_at":"2026-08-12T12:27:12.148202Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:27:12.131708Z","title":"Srformer: Permuted self-attention for single image super-resolution,","venue":null,"work_id":"b00c28cd-17ac-4636-8feb-1edbc18cc942","year":2023},"citing_paper":{"arxiv_id":"2411.17214","last_updated":"2025-03-19T03:09:55Z","snapshot_observed_at":"2026-08-12T12:20:27.476209Z","submitted_at":"2024-11-26T08:30:31Z","title":"MAT: Multi-Range Attention Transformer for Efficient Image Super-Resolution","version":3},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:11.440296Z"},"links":{"citing_paper":"/paper/2411.17214"},"observation_digest":"sha256:3f9622137c3be34db80b58749c8826c3726feb092370e3901b47f4ae6d316e78","observation_id":"65ea4c5d-445c-4d18-bd07-17920e565b26","resolution":{"observed_at":"2026-08-12T12:27:12.135550Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:27:12.120423Z","title":"Scaling up your kernels to 31x31: Revisiting large kernel design in cnns,","venue":null,"work_id":"89cf45ec-a6b7-4f6a-91b7-0dbad1b1051e","year":2022},"citing_paper":{"arxiv_id":"2411.17214","last_updated":"2025-03-19T03:09:55Z","snapshot_observed_at":"2026-08-12T12:20:27.476209Z","submitted_at":"2024-11-26T08:30:31Z","title":"MAT: Multi-Range Attention Transformer for Efficient Image Super-Resolution","version":3},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:11.444226Z"},"links":{"citing_paper":"/paper/2411.17214"},"observation_digest":"sha256:3e1d040b30d4da57e699589244f8416dddc8a64bd68a302df374aee0c2e69477","observation_id":"40bc7f20-9eac-44dc-b672-8eacc094491f","resolution":{"observed_at":"2026-08-12T12:27:12.124052Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:27:12.109065Z","title":"Efficient and explicit modelling of image hierarchies for image restoration,","venue":null,"work_id":"cdbee4f4-5831-4812-ba7f-677dc503057d","year":2023},"citing_paper":{"arxiv_id":"2411.17214","last_updated":"2025-03-19T03:09:55Z","snapshot_observed_at":"2026-08-12T12:20:27.476209Z","submitted_at":"2024-11-26T08:30:31Z","title":"MAT: Multi-Range Attention Transformer for Efficient Image Super-Resolution","version":3},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:11.449115Z"},"links":{"citing_paper":"/paper/2411.17214"},"observation_digest":"sha256:b5e55953eef5066b7844fe533256cfdf70fe7de46a722b5385ad17ecf6e5998b","observation_id":"db5d94aa-6853-43fd-8db6-8a4d622f5ca8","resolution":{"observed_at":"2026-08-12T12:27:12.113840Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1706.05587","last_updated":"2017-12-05T18:06:21Z","snapshot_observed_at":"2026-08-07T13:44:53.690521Z","submitted_at":"2017-06-17T22:48:57Z","title":"Rethinking Atrous Convolution for Semantic Image Segmentation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1706.05587","snapshot_observed_at":"2026-08-12T12:27:11.452851Z","title":"Rethinking atrous convolution for semantic image segmentation,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2411.17214","last_updated":"2025-03-19T03:09:55Z","snapshot_observed_at":"2026-08-12T12:20:27.476209Z","submitted_at":"2024-11-26T08:30:31Z","title":"MAT: Multi-Range Attention Transformer for Efficient Image Super-Resolution","version":3},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:11.452851Z"},"links":{"cited_paper":"/paper/1706.05587","citing_paper":"/paper/2411.17214"},"observation_digest":"sha256:2f66ce7452e2b9e035c361c6daff0ec6f40734f2bd8e3df5a9a8e1af3fcd40a0","observation_id":"2f4f4697-41c2-4549-b657-4d2b1bd022d3","resolution":{"observed_at":"2026-08-12T12:27:11.452851Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:27:12.098232Z","title":"Understanding convolution for semantic segmentation,","venue":null,"work_id":"13e447c6-5787-4027-8fcd-7ea7fe98a8f6","year":2018},"citing_paper":{"arxiv_id":"2411.17214","last_updated":"2025-03-19T03:09:55Z","snapshot_observed_at":"2026-08-12T12:20:27.476209Z","submitted_at":"2024-11-26T08:30:31Z","title":"MAT: Multi-Range Attention Transformer for Efficient Image Super-Resolution","version":3},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:11.456641Z"},"links":{"citing_paper":"/paper/2411.17214"},"observation_digest":"sha256:0517e92dbf42ecdba8d63c93454a9e0ce7d2ca0b8b03a9f864bfd747283ae26e","observation_id":"b6e172c6-db67-43fc-b2dc-0561c5f4f312","resolution":{"observed_at":"2026-08-12T12:27:12.102219Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:27:12.087589Z","title":"Rewrite the stars,","venue":null,"work_id":"d5db6a56-b69d-42eb-95f4-20f288d774b2","year":2024},"citing_paper":{"arxiv_id":"2411.17214","last_updated":"2025-03-19T03:09:55Z","snapshot_observed_at":"2026-08-12T12:20:27.476209Z","submitted_at":"2024-11-26T08:30:31Z","title":"MAT: Multi-Range Attention Transformer for Efficient Image Super-Resolution","version":3},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:11.460949Z"},"links":{"citing_paper":"/paper/2411.17214"},"observation_digest":"sha256:b14b8f08bc81bf71ea261c9bc4e63a86bb3e8d975d3e1e64997ed384bbcdbd9f","observation_id":"7f4403dd-e189-4497-ab51-580296524172","resolution":{"observed_at":"2026-08-12T12:27:12.091367Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:27:12.076501Z","title":"Ntire 2017 challenge on single image super-resolution: Dataset and study,","venue":null,"work_id":"0313bd55-0ea6-4a03-a041-d4cc4a319043","year":2017},"citing_paper":{"arxiv_id":"2411.17214","last_updated":"2025-03-19T03:09:55Z","snapshot_observed_at":"2026-08-12T12:20:27.476209Z","submitted_at":"2024-11-26T08:30:31Z","title":"MAT: Multi-Range Attention Transformer for Efficient Image Super-Resolution","version":3},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:11.465004Z"},"links":{"citing_paper":"/paper/2411.17214"},"observation_digest":"sha256:84a3393022df6644e3bcab2f48d20358365d1710e8ce7647a7b11b507e5bac86","observation_id":"58c1bbd8-d1cc-4eab-ac29-9a38b0caff2f","resolution":{"observed_at":"2026-08-12T12:27:12.080650Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:27:12.064101Z","title":"Batch normalization: Accelerating deep network training by reducing internal covariate shift,","venue":null,"work_id":"cd1f2d50-95fa-4c2a-a248-3c4f0e7b8d45","year":2015},"citing_paper":{"arxiv_id":"2411.17214","last_updated":"2025-03-19T03:09:55Z","snapshot_observed_at":"2026-08-12T12:20:27.476209Z","submitted_at":"2024-11-26T08:30:31Z","title":"MAT: Multi-Range Attention Transformer for Efficient Image Super-Resolution","version":3},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:11.468866Z"},"links":{"citing_paper":"/paper/2411.17214"},"observation_digest":"sha256:1d6b2caa911e759741679f61cecab5b97dd047e5c9745eaf9fc51f45b7ca115d","observation_id":"482d88e8-e00c-47ca-a04a-a5f97a7e9239","resolution":{"observed_at":"2026-08-12T12:27:12.068360Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:27:12.052643Z","title":"Cross-scale internal graph neural network for image super-resolution,","venue":null,"work_id":"c1b82f3e-5a87-4685-8632-07ab545c69b1","year":2020},"citing_paper":{"arxiv_id":"2411.17214","last_updated":"2025-03-19T03:09:55Z","snapshot_observed_at":"2026-08-12T12:20:27.476209Z","submitted_at":"2024-11-26T08:30:31Z","title":"MAT: Multi-Range Attention Transformer for Efficient Image Super-Resolution","version":3},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:11.472486Z"},"links":{"citing_paper":"/paper/2411.17214"},"observation_digest":"sha256:5f6954c5391a0c19f96ada00187b28ce812f43c583b87d227f21c3e9836581a2","observation_id":"5b719131-3640-4cef-a20b-a36da30f9e7c","resolution":{"observed_at":"2026-08-12T12:27:12.056520Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:27:12.040752Z","title":"Feedback pyramid attention networks for single image super-resolution,","venue":null,"work_id":"36937487-30d0-432c-857f-f561bc42c189","year":2023},"citing_paper":{"arxiv_id":"2411.17214","last_updated":"2025-03-19T03:09:55Z","snapshot_observed_at":"2026-08-12T12:20:27.476209Z","submitted_at":"2024-11-26T08:30:31Z","title":"MAT: Multi-Range Attention Transformer for Efficient Image Super-Resolution","version":3},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:11.476233Z"},"links":{"citing_paper":"/paper/2411.17214"},"observation_digest":"sha256:92e0be770b3eaaa80cd1575148c2ab26fef730d93ac69d4b6af1f76849864b51","observation_id":"eb27495a-d0cf-4831-bf85-b53bb0ae5756","resolution":{"observed_at":"2026-08-12T12:27:12.044829Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:27:12.028110Z","title":"Non-local neural net- works,","venue":null,"work_id":"e71ae9a1-ea15-4e39-b2a1-fb77b1ee9ca3","year":2018},"citing_paper":{"arxiv_id":"2411.17214","last_updated":"2025-03-19T03:09:55Z","snapshot_observed_at":"2026-08-12T12:20:27.476209Z","submitted_at":"2024-11-26T08:30:31Z","title":"MAT: Multi-Range Attention Transformer for Efficient Image Super-Resolution","version":3},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:11.480421Z"},"links":{"citing_paper":"/paper/2411.17214"},"observation_digest":"sha256:8b096b85140ee2962a2a93dad9bf4e08c9bce7b3f9ba3a22881fbd3355f69ea7","observation_id":"f3e61d38-3461-4cf7-9dfc-92f5787c9c2c","resolution":{"observed_at":"2026-08-12T12:27:12.031882Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:27:12.016572Z","title":"An image is worth 16x16 words: Transformers for image recognition at scale,","venue":null,"work_id":"d7073203-c87d-4987-8057-c450bb935e41","year":2021},"citing_paper":{"arxiv_id":"2411.17214","last_updated":"2025-03-19T03:09:55Z","snapshot_observed_at":"2026-08-12T12:20:27.476209Z","submitted_at":"2024-11-26T08:30:31Z","title":"MAT: Multi-Range Attention Transformer for Efficient Image Super-Resolution","version":3},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:11.484132Z"},"links":{"citing_paper":"/paper/2411.17214"},"observation_digest":"sha256:4befaa69635cfc114d2b2dabb9faac505cc7614738c047b27bc9d789fdaef9f3","observation_id":"e6eb5a2b-a700-4a97-91a3-4eacfb007bf5","resolution":{"observed_at":"2026-08-12T12:27:12.020443Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:27:12.004370Z","title":"Swin transformer: Hierarchical vision transformer using shifted windows,","venue":null,"work_id":"2a9cfa25-a68e-467c-bf3e-9a14a0bfda39","year":2021},"citing_paper":{"arxiv_id":"2411.17214","last_updated":"2025-03-19T03:09:55Z","snapshot_observed_at":"2026-08-12T12:20:27.476209Z","submitted_at":"2024-11-26T08:30:31Z","title":"MAT: Multi-Range Attention Transformer for Efficient Image Super-Resolution","version":3},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:11.488544Z"},"links":{"citing_paper":"/paper/2411.17214"},"observation_digest":"sha256:893c07370270bcb90ea2dc6215e3f86c16f4822f27bf7966bce526c3c17d8e9c","observation_id":"a429f530-19eb-417a-b5d0-e490b81e46b3","resolution":{"observed_at":"2026-08-12T12:27:12.009099Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:27:11.989636Z","title":"Segformer: Simple and efficient design for semantic segmentation with transformers,","venue":null,"work_id":"fd4bf439-fb80-417d-987e-70ef49b7b795","year":2021},"citing_paper":{"arxiv_id":"2411.17214","last_updated":"2025-03-19T03:09:55Z","snapshot_observed_at":"2026-08-12T12:20:27.476209Z","submitted_at":"2024-11-26T08:30:31Z","title":"MAT: Multi-Range Attention Transformer for Efficient Image Super-Resolution","version":3},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:11.492120Z"},"links":{"citing_paper":"/paper/2411.17214"},"observation_digest":"sha256:e681af6719a126dedae0b04a6b9d980fa2e52b9248a499c9c9c845499b5ebe84","observation_id":"ade6d452-777e-4a14-b95a-fc912b9bd9bb","resolution":{"observed_at":"2026-08-12T12:27:11.994555Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:27:11.978656Z","title":"End-to-end object detection with transformers,","venue":null,"work_id":"d169e25a-6932-4acd-8368-60bbab8d98ca","year":2020},"citing_paper":{"arxiv_id":"2411.17214","last_updated":"2025-03-19T03:09:55Z","snapshot_observed_at":"2026-08-12T12:20:27.476209Z","submitted_at":"2024-11-26T08:30:31Z","title":"MAT: Multi-Range Attention Transformer for Efficient Image Super-Resolution","version":3},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:11.496097Z"},"links":{"citing_paper":"/paper/2411.17214"},"observation_digest":"sha256:c0259e00627f77b7ee499f02b685ab453ce69277f570ce17616f982e09ed5252","observation_id":"961294f6-ac90-4f0d-b450-05501ec7dd14","resolution":{"observed_at":"2026-08-12T12:27:11.982583Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:27:11.966842Z","title":"Restormer: Efficient transformer for high-resolution image restoration,","venue":null,"work_id":"ceff2044-074d-4c8b-81b6-8a75c46105b1","year":2022},"citing_paper":{"arxiv_id":"2411.17214","last_updated":"2025-03-19T03:09:55Z","snapshot_observed_at":"2026-08-12T12:20:27.476209Z","submitted_at":"2024-11-26T08:30:31Z","title":"MAT: Multi-Range Attention Transformer for Efficient Image Super-Resolution","version":3},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:11.499987Z"},"links":{"citing_paper":"/paper/2411.17214"},"observation_digest":"sha256:9eb3877bb818a6d8e8767565c1f726ba1b20deb60e10d3a69fa53f07431abefd","observation_id":"d2a6a427-0214-4fe3-93a5-9ed95a72f83b","resolution":{"observed_at":"2026-08-12T12:27:11.971035Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:27:11.953452Z","title":"Egocentric early action prediction via multimodal transformer-based dual action prediction,","venue":null,"work_id":"f4f753df-1aec-4125-a838-847126fabe01","year":null},"citing_paper":{"arxiv_id":"2411.17214","last_updated":"2025-03-19T03:09:55Z","snapshot_observed_at":"2026-08-12T12:20:27.476209Z","submitted_at":"2024-11-26T08:30:31Z","title":"MAT: Multi-Range Attention Transformer for Efficient Image Super-Resolution","version":3},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:11.503701Z"},"links":{"citing_paper":"/paper/2411.17214"},"observation_digest":"sha256:45dc6db6edecfb1868c33dd1760cb625b55d295d71ef7ce856d492d01208e8b0","observation_id":"346c986b-50f4-40d6-b671-fb3053930819","resolution":{"observed_at":"2026-08-12T12:27:11.958782Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:27:11.941633Z","title":"Msvt: Multiple spatiotemporal views transformer for deepfake video detection,","venue":null,"work_id":"15e0051a-ece9-4675-b97c-37577fb0fcd4","year":2023},"citing_paper":{"arxiv_id":"2411.17214","last_updated":"2025-03-19T03:09:55Z","snapshot_observed_at":"2026-08-12T12:20:27.476209Z","submitted_at":"2024-11-26T08:30:31Z","title":"MAT: Multi-Range Attention Transformer for Efficient Image Super-Resolution","version":3},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:11.507589Z"},"links":{"citing_paper":"/paper/2411.17214"},"observation_digest":"sha256:8c0fdf957d49ca92516378959dcc12b229ac81715168470790b4f9968292b57c","observation_id":"a379a477-f2a3-47ed-927d-cc5823760830","resolution":{"observed_at":"2026-08-12T12:27:11.946003Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:27:11.930561Z","title":"Lightweight image super-resolution with pyramid clustering transformer,","venue":null,"work_id":"84ba6655-62c1-4c45-9143-dfcc0681653a","year":2023},"citing_paper":{"arxiv_id":"2411.17214","last_updated":"2025-03-19T03:09:55Z","snapshot_observed_at":"2026-08-12T12:20:27.476209Z","submitted_at":"2024-11-26T08:30:31Z","title":"MAT: Multi-Range Attention Transformer for Efficient Image Super-Resolution","version":3},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:11.511050Z"},"links":{"citing_paper":"/paper/2411.17214"},"observation_digest":"sha256:a62b9ab0f999f65073c66fda1ad9be83e1144f74a860a347e65c8f0f0a5b7177","observation_id":"540252f5-a05d-447e-92e0-66ed7ff1af5b","resolution":{"observed_at":"2026-08-12T12:27:11.934278Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:27:11.918719Z","title":"Hybrid attention- based u-shaped network for remote sensing image super-resolution,","venue":null,"work_id":"34ad0b79-0bed-428e-9786-86fd1df55fc0","year":2023},"citing_paper":{"arxiv_id":"2411.17214","last_updated":"2025-03-19T03:09:55Z","snapshot_observed_at":"2026-08-12T12:20:27.476209Z","submitted_at":"2024-11-26T08:30:31Z","title":"MAT: Multi-Range Attention Transformer for Efficient Image Super-Resolution","version":3},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:11.515200Z"},"links":{"citing_paper":"/paper/2411.17214"},"observation_digest":"sha256:cdd458b08333339b6ab1cb9ba93c1609a652b7d199d63d766f1e432819062bc6","observation_id":"52e2b562-2844-4210-be84-8bf3339b5a2f","resolution":{"observed_at":"2026-08-12T12:27:11.922498Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:27:11.906852Z","title":"Cross-spatial pixel integration and cross-stage feature fusion- based transformer network for remote sensing image super-resolution,","venue":null,"work_id":"8477779e-896d-4748-aad7-5759c411c444","year":2023},"citing_paper":{"arxiv_id":"2411.17214","last_updated":"2025-03-19T03:09:55Z","snapshot_observed_at":"2026-08-12T12:20:27.476209Z","submitted_at":"2024-11-26T08:30:31Z","title":"MAT: Multi-Range Attention Transformer for Efficient Image Super-Resolution","version":3},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:11.518862Z"},"links":{"citing_paper":"/paper/2411.17214"},"observation_digest":"sha256:2051022ca96a8529d0c55da0205456e918bdecd659174749ff60bc7e1b0c04a2","observation_id":"1d24a3a4-4b31-4dc3-8ed2-55c791942532","resolution":{"observed_at":"2026-08-12T12:27:11.911054Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:27:11.895332Z","title":"Omni aggregation networks for lightweight image super-resolution,","venue":null,"work_id":"1fe4ce04-728a-4b30-9ef7-f17d6287bed8","year":2023},"citing_paper":{"arxiv_id":"2411.17214","last_updated":"2025-03-19T03:09:55Z","snapshot_observed_at":"2026-08-12T12:20:27.476209Z","submitted_at":"2024-11-26T08:30:31Z","title":"MAT: Multi-Range Attention Transformer for Efficient Image Super-Resolution","version":3},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:11.522527Z"},"links":{"citing_paper":"/paper/2411.17214"},"observation_digest":"sha256:80754a0349228a808092dd71d6e7da14c85090844dfa6037015d7f07163b1eab","observation_id":"55b84574-5d7c-4297-b5f8-18f55ad78a34","resolution":{"observed_at":"2026-08-12T12:27:11.900041Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:27:11.884161Z","title":"Accurate image restoration with attention retractable transformer,","venue":null,"work_id":"c3b665ba-38ae-4987-86e8-a14a0a240848","year":2023},"citing_paper":{"arxiv_id":"2411.17214","last_updated":"2025-03-19T03:09:55Z","snapshot_observed_at":"2026-08-12T12:20:27.476209Z","submitted_at":"2024-11-26T08:30:31Z","title":"MAT: Multi-Range Attention Transformer for Efficient Image Super-Resolution","version":3},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:11.527153Z"},"links":{"citing_paper":"/paper/2411.17214"},"observation_digest":"sha256:f957ac797fb665da2c8e567a0012dffa2774e01b054607a1c7b1cd3ff6a696d4","observation_id":"13b46480-3614-4ca2-a54e-89d254543df2","resolution":{"observed_at":"2026-08-12T12:27:11.888371Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:27:11.870247Z","title":"Transformer-based dual-branch multiscale fusion network for pan-sharpening remote sensing images,","venue":null,"work_id":"b75671f1-4628-481f-bcc4-2f0e5a986ba8","year":2023},"citing_paper":{"arxiv_id":"2411.17214","last_updated":"2025-03-19T03:09:55Z","snapshot_observed_at":"2026-08-12T12:20:27.476209Z","submitted_at":"2024-11-26T08:30:31Z","title":"MAT: Multi-Range Attention Transformer for Efficient Image Super-Resolution","version":3},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:11.530551Z"},"links":{"citing_paper":"/paper/2411.17214"},"observation_digest":"sha256:f1a3dbeb9010e4e926e3333643c88f5b75fb1a8be3c0958ad34209d39969d7ac","observation_id":"59cf3ffb-a696-4ec8-b512-29f2c5feac35","resolution":{"observed_at":"2026-08-12T12:27:11.875394Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:27:11.858283Z","title":"Slide-transformer: Hierarchical vision transformer with local self-attention,","venue":null,"work_id":"21445dfa-f9c3-4a0f-bcc3-455c8f5d69c0","year":2023},"citing_paper":{"arxiv_id":"2411.17214","last_updated":"2025-03-19T03:09:55Z","snapshot_observed_at":"2026-08-12T12:20:27.476209Z","submitted_at":"2024-11-26T08:30:31Z","title":"MAT: Multi-Range Attention Transformer for Efficient Image Super-Resolution","version":3},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:11.534056Z"},"links":{"citing_paper":"/paper/2411.17214"},"observation_digest":"sha256:ce0bd20d6fd10e6276e37eddc2007335b17f3176fbdd9a2845419aea923087ba","observation_id":"7ab3a0f2-ba26-4bf1-8da5-fd14cf4070fe","resolution":{"observed_at":"2026-08-12T12:27:11.862222Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:27:11.847175Z","title":"Dilateformer: Multi-scale dilated transformer for visual recognition,","venue":null,"work_id":"55e73e15-ca0a-4599-81a8-2f0c8b04d548","year":2023},"citing_paper":{"arxiv_id":"2411.17214","last_updated":"2025-03-19T03:09:55Z","snapshot_observed_at":"2026-08-12T12:20:27.476209Z","submitted_at":"2024-11-26T08:30:31Z","title":"MAT: Multi-Range Attention Transformer for Efficient Image Super-Resolution","version":3},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:11.537815Z"},"links":{"citing_paper":"/paper/2411.17214"},"observation_digest":"sha256:732a2f91e893f459aa004bf926b5b83c1eb006e8dc1f4d4075329af65042cad8","observation_id":"912e21f1-9f43-4443-a8bc-e85e07e0ed0d","resolution":{"observed_at":"2026-08-12T12:27:11.850749Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:27:11.836275Z","title":"Real-time single image and video super- resolution using an efficient sub-pixel convolutional neural network,","venue":null,"work_id":"b8a0e999-a081-43b8-8bf9-0ba6571fd688","year":2016},"citing_paper":{"arxiv_id":"2411.17214","last_updated":"2025-03-19T03:09:55Z","snapshot_observed_at":"2026-08-12T12:20:27.476209Z","submitted_at":"2024-11-26T08:30:31Z","title":"MAT: Multi-Range Attention Transformer for Efficient Image Super-Resolution","version":3},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:11.541552Z"},"links":{"citing_paper":"/paper/2411.17214"},"observation_digest":"sha256:7b69bba314d56d7b498d3c33ad9ced3681c12388502c2b809be18943b32976ab","observation_id":"e0e8abaf-923d-4c03-aedb-1ab2fd4a142b","resolution":{"observed_at":"2026-08-12T12:27:11.840174Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:27:11.825029Z","title":"Efficient non- local contrastive attention for image super-resolution,","venue":null,"work_id":"2989128e-add2-4b9b-9ace-149e463044eb","year":2022},"citing_paper":{"arxiv_id":"2411.17214","last_updated":"2025-03-19T03:09:55Z","snapshot_observed_at":"2026-08-12T12:20:27.476209Z","submitted_at":"2024-11-26T08:30:31Z","title":"MAT: Multi-Range Attention Transformer for Efficient Image Super-Resolution","version":3},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:11.545966Z"},"links":{"citing_paper":"/paper/2411.17214"},"observation_digest":"sha256:399b488fab872e9f9ce15e1895334ce6bceb0f489e2dcddea9a6e93c5d04bf43","observation_id":"b012ea17-7930-495f-a813-092131796ef2","resolution":{"observed_at":"2026-08-12T12:27:11.828922Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:27:11.813119Z","title":"Stand-alone self-attention in vision models,","venue":null,"work_id":"94971c7e-2714-4192-ba79-c35a93f68086","year":2019},"citing_paper":{"arxiv_id":"2411.17214","last_updated":"2025-03-19T03:09:55Z","snapshot_observed_at":"2026-08-12T12:20:27.476209Z","submitted_at":"2024-11-26T08:30:31Z","title":"MAT: Multi-Range Attention Transformer for Efficient Image Super-Resolution","version":3},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:11.549680Z"},"links":{"citing_paper":"/paper/2411.17214"},"observation_digest":"sha256:13717fc42f44172e80a188d6ec7c45396315b7fea6d9319b26ed14a24faf3b5a","observation_id":"cb8082bb-2650-452d-88d6-67d494aca098","resolution":{"observed_at":"2026-08-12T12:27:11.817650Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:27:11.801493Z","title":"Neighborhood attention transformer,","venue":null,"work_id":"1f64bc02-4482-44ef-9588-c631151e541f","year":2023},"citing_paper":{"arxiv_id":"2411.17214","last_updated":"2025-03-19T03:09:55Z","snapshot_observed_at":"2026-08-12T12:20:27.476209Z","submitted_at":"2024-11-26T08:30:31Z","title":"MAT: Multi-Range Attention Transformer for Efficient Image Super-Resolution","version":3},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:11.553147Z"},"links":{"citing_paper":"/paper/2411.17214"},"observation_digest":"sha256:78d59f31044e819440e61461795e4332bd4b40a2d910754227925fed08121888","observation_id":"9687e7b6-84ff-4797-91bb-b5b1fe6661a0","resolution":{"observed_at":"2026-08-12T12:27:11.805250Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:27:11.789013Z","title":"Scaling local self-attention for parameter efficient visual backbones,","venue":null,"work_id":"9a9aa494-7146-43ae-947f-c37f25161408","year":2021},"citing_paper":{"arxiv_id":"2411.17214","last_updated":"2025-03-19T03:09:55Z","snapshot_observed_at":"2026-08-12T12:20:27.476209Z","submitted_at":"2024-11-26T08:30:31Z","title":"MAT: Multi-Range Attention Transformer for Efficient Image Super-Resolution","version":3},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:11.556962Z"},"links":{"citing_paper":"/paper/2411.17214"},"observation_digest":"sha256:6282fb2267241e07edd7f6af0c2227bcbd4243cff92f7eb2073b09909125a411","observation_id":"13fdde39-941e-4283-a21f-601c97b424ee","resolution":{"observed_at":"2026-08-12T12:27:11.793042Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:27:11.777791Z","title":"Exploring frequency-inspired optimization in transformer for efficient single image super-resolution,","venue":null,"work_id":"296a9625-c772-4a7f-89d7-828f75a8fa4d","year":2025},"citing_paper":{"arxiv_id":"2411.17214","last_updated":"2025-03-19T03:09:55Z","snapshot_observed_at":"2026-08-12T12:20:27.476209Z","submitted_at":"2024-11-26T08:30:31Z","title":"MAT: Multi-Range Attention Transformer for Efficient Image Super-Resolution","version":3},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:11.561066Z"},"links":{"citing_paper":"/paper/2411.17214"},"observation_digest":"sha256:585afc72f9069a9641e23459d0c7a2f92dc41f152a79432f2894471ad77dbed5","observation_id":"0e3ff83e-60c3-4140-88a0-4ce3e10bf30a","resolution":{"observed_at":"2026-08-12T12:27:11.781476Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:27:11.765387Z","title":"Low- complexity single-image super-resolution based on nonnegative neighbor embedding,","venue":null,"work_id":"08b2e6db-3185-40fb-bd19-a322f7d3f47a","year":2012},"citing_paper":{"arxiv_id":"2411.17214","last_updated":"2025-03-19T03:09:55Z","snapshot_observed_at":"2026-08-12T12:20:27.476209Z","submitted_at":"2024-11-26T08:30:31Z","title":"MAT: Multi-Range Attention Transformer for Efficient Image Super-Resolution","version":3},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:11.564576Z"},"links":{"citing_paper":"/paper/2411.17214"},"observation_digest":"sha256:e6a609758bdc32f0bae9690f0028f4cd84353eb88f5174b582125a3fac1ee610","observation_id":"2c2ab9b4-18bc-4633-a99e-4d84697c8331","resolution":{"observed_at":"2026-08-12T12:27:11.769632Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:27:11.752448Z","title":"On single image scale-up using sparse-representations,","venue":null,"work_id":"48740b8c-a917-4216-bb79-a8b7a1ef1839","year":2012},"citing_paper":{"arxiv_id":"2411.17214","last_updated":"2025-03-19T03:09:55Z","snapshot_observed_at":"2026-08-12T12:20:27.476209Z","submitted_at":"2024-11-26T08:30:31Z","title":"MAT: Multi-Range Attention Transformer for Efficient Image Super-Resolution","version":3},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:11.568766Z"},"links":{"citing_paper":"/paper/2411.17214"},"observation_digest":"sha256:e27315375d31141c28fa88ac02b5ee3ac1374b1bb32ff5b2874b449ccbbedbeb","observation_id":"9f5136cd-4539-4e41-8442-7b51baea5062","resolution":{"observed_at":"2026-08-12T12:27:11.757237Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:27:11.740174Z","title":"A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics,","venue":null,"work_id":"808c5b63-ca11-43c2-9a37-56f5b4f6b25b","year":2001},"citing_paper":{"arxiv_id":"2411.17214","last_updated":"2025-03-19T03:09:55Z","snapshot_observed_at":"2026-08-12T12:20:27.476209Z","submitted_at":"2024-11-26T08:30:31Z","title":"MAT: Multi-Range Attention Transformer for Efficient Image Super-Resolution","version":3},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:11.572365Z"},"links":{"citing_paper":"/paper/2411.17214"},"observation_digest":"sha256:2d9c5949e60e1477676759cdd200f8ca3ce4d42b24bd759d944a79dab9673923","observation_id":"6681d7be-2626-49d9-9ad1-28ca57445765","resolution":{"observed_at":"2026-08-12T12:27:11.744447Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:27:11.728573Z","title":"Sketch-based manga retrieval using manga109 dataset,","venue":null,"work_id":"bb685f5c-87f0-4568-b119-18a8135c6b92","year":2017},"citing_paper":{"arxiv_id":"2411.17214","last_updated":"2025-03-19T03:09:55Z","snapshot_observed_at":"2026-08-12T12:20:27.476209Z","submitted_at":"2024-11-26T08:30:31Z","title":"MAT: Multi-Range Attention Transformer for Efficient Image Super-Resolution","version":3},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:11.575957Z"},"links":{"citing_paper":"/paper/2411.17214"},"observation_digest":"sha256:559b2d4b42630cfef6ea385b7bca683514eeb6c1a1599d683259dfbc66ac66dc","observation_id":"0685b978-9e31-4b9f-b6c7-b5fd765196a6","resolution":{"observed_at":"2026-08-12T12:27:11.732575Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1412.6980","last_updated":"2017-01-30T01:27:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2014-12-22T13:54:29Z","title":"Adam: A Method for Stochastic Optimization","version":9},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1412.6980","snapshot_observed_at":"2026-08-12T12:27:11.579601Z","title":"Adam: A method for stochastic optimization,","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2411.17214","last_updated":"2025-03-19T03:09:55Z","snapshot_observed_at":"2026-08-12T12:20:27.476209Z","submitted_at":"2024-11-26T08:30:31Z","title":"MAT: Multi-Range Attention Transformer for Efficient Image Super-Resolution","version":3},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:11.579601Z"},"links":{"cited_paper":"/paper/1412.6980","citing_paper":"/paper/2411.17214"},"observation_digest":"sha256:195b4fccea68c4cd91db94651375a4e3f6f8fb9ee32856d395e774c526b168ec","observation_id":"408ee3bb-f3a6-4ccc-ab4e-c20b0bd9394a","resolution":{"observed_at":"2026-08-12T12:27:11.579601Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:27:11.717571Z","title":"Latticenet: Towards lightweight image super-resolution with lattice block,","venue":null,"work_id":"1a87f8cc-db19-4fcd-9890-5277a91434f8","year":2020},"citing_paper":{"arxiv_id":"2411.17214","last_updated":"2025-03-19T03:09:55Z","snapshot_observed_at":"2026-08-12T12:20:27.476209Z","submitted_at":"2024-11-26T08:30:31Z","title":"MAT: Multi-Range Attention Transformer for Efficient Image Super-Resolution","version":3},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:11.584215Z"},"links":{"citing_paper":"/paper/2411.17214"},"observation_digest":"sha256:0111d060d6478a6fbb93fa7ccbdcb3b2a02b1fbb7a544aaf9e2178114fee13c8","observation_id":"9754f7b8-0086-4acc-bd5b-52ef10dbe890","resolution":{"observed_at":"2026-08-12T12:27:11.721493Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:27:11.704362Z","title":"N-gram in swin transformers for efficient lightweight image super-resolution,","venue":null,"work_id":"1f189f70-6e06-48e1-a219-f348a7b608a2","year":2023},"citing_paper":{"arxiv_id":"2411.17214","last_updated":"2025-03-19T03:09:55Z","snapshot_observed_at":"2026-08-12T12:20:27.476209Z","submitted_at":"2024-11-26T08:30:31Z","title":"MAT: Multi-Range Attention Transformer for Efficient Image Super-Resolution","version":3},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:11.588299Z"},"links":{"citing_paper":"/paper/2411.17214"},"observation_digest":"sha256:95bc01ca6901a5eb808744cdae259c077838d1fb36b83892b60af65570b6c2e1","observation_id":"cfca8485-c9e4-4e3c-8f13-3af5906602d9","resolution":{"observed_at":"2026-08-12T12:27:11.709226Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:27:11.692828Z","title":"Mambair: A simple baseline for image restoration with state-space model,","venue":null,"work_id":"f6ad304d-9721-44e6-9967-71cdc950bd17","year":null},"citing_paper":{"arxiv_id":"2411.17214","last_updated":"2025-03-19T03:09:55Z","snapshot_observed_at":"2026-08-12T12:20:27.476209Z","submitted_at":"2024-11-26T08:30:31Z","title":"MAT: Multi-Range Attention Transformer for Efficient Image Super-Resolution","version":3},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:11.591845Z"},"links":{"citing_paper":"/paper/2411.17214"},"observation_digest":"sha256:5649bf3409bc2dd1e4f2611906846942f43760c690befaffe6554a364d908768","observation_id":"50d74ff6-ab36-4120-bbfc-f09ff49cf13a","resolution":{"observed_at":"2026-08-12T12:27:11.696824Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:27:11.680588Z","title":"Srconvnet: A transformer-style convnet for lightweight image super-resolution,","venue":null,"work_id":"69a6c310-a076-4e9e-80b9-c3e18bea5d94","year":2025},"citing_paper":{"arxiv_id":"2411.17214","last_updated":"2025-03-19T03:09:55Z","snapshot_observed_at":"2026-08-12T12:20:27.476209Z","submitted_at":"2024-11-26T08:30:31Z","title":"MAT: Multi-Range Attention Transformer for Efficient Image Super-Resolution","version":3},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:11.595464Z"},"links":{"citing_paper":"/paper/2411.17214"},"observation_digest":"sha256:9709845d4b465c0c46a6f8ba5d8d06c1d6826d623eb44f27a94b2944b3fc655a","observation_id":"64479224-da2c-4ab3-99d2-fdfd8d3b0eb5","resolution":{"observed_at":"2026-08-12T12:27:11.684542Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:27:11.669713Z","title":"Dual aggregation transformer for image super-resolution,","venue":null,"work_id":"1b6e4ac2-bad1-4d2f-80d8-0e902504868f","year":2023},"citing_paper":{"arxiv_id":"2411.17214","last_updated":"2025-03-19T03:09:55Z","snapshot_observed_at":"2026-08-12T12:20:27.476209Z","submitted_at":"2024-11-26T08:30:31Z","title":"MAT: Multi-Range Attention Transformer for Efficient Image Super-Resolution","version":3},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:11.599868Z"},"links":{"citing_paper":"/paper/2411.17214"},"observation_digest":"sha256:84284791f583e2ecb684480a8eefaace53e700d66b5bf1ed91325cc6cd62459d","observation_id":"7915ef26-db1c-4da6-b78d-582301b09e5e","resolution":{"observed_at":"2026-08-12T12:27:11.673642Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:27:11.656210Z","title":"Image super- resolution via efficient transformer embedding frequency decomposition with restart,","venue":null,"work_id":"3ba3b4c7-b922-4ed1-8346-d6f3be8e1c97","year":2024},"citing_paper":{"arxiv_id":"2411.17214","last_updated":"2025-03-19T03:09:55Z","snapshot_observed_at":"2026-08-12T12:20:27.476209Z","submitted_at":"2024-11-26T08:30:31Z","title":"MAT: Multi-Range Attention Transformer for Efficient Image Super-Resolution","version":3},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-12T12:27:11.604333Z"},"links":{"citing_paper":"/paper/2411.17214"},"observation_digest":"sha256:21536d418767006fdae883f88bf1146c75b47342d337b240a5a3a63fa5841e8b","observation_id":"8ef2dd35-ca97-434f-bba0-96e946af96f5","resolution":{"observed_at":"2026-08-12T12:27:11.661861Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2411.17214","last_updated":"2025-03-19T03:09:55Z","latest_version":3,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-12T12:20:27.476209Z","submitted_at":"2024-11-26T08:30:31Z","title":"MAT: Multi-Range Attention Transformer for Efficient Image Super-Resolution"},"reference_resolution":{"displayed":65,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":2,"verified_exact":0,"verified_fuzzy":63},"total_outbound_references":65},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"thesis":"As of 12 August 2026, this Paper Citation Record lists 65 of 65 outbound references and 0 inbound Pith citation observations for arXiv:2411.17214."}