{"as_of":"2026-08-06T14:16:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:48bea368227dc2ae3d8c81b27ebf606a8dc3aceb9656f4e3344db75c3e3c65fb","coverage":[{"denominator":49,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":49,"source":"paper_references, paper_reference_links","source_observed_at":"2026-07-14T19:35:19.592168Z","state":"measured"},{"denominator":50,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":50,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-06T06:34:29.942622+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-07-11T11:50:26.030339Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-06-29T00:22:50.689041Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2604.22823","last_updated":"2026-07-13T15:11:18Z","snapshot_observed_at":"2026-07-16T23:19:34.740600Z","submitted_at":"2026-04-18T09:38:03Z","title":"PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging","version":2},"cited_work":{"arxiv_id":"2604.22823","doi":null,"metadata_source":"pith","pith_arxiv_id":"2604.22823","snapshot_observed_at":"2026-06-29T00:22:50.689041Z","title":"PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging","venue":"cs.CV","work_id":"39690631-85df-47f0-9c1a-2a4cd0316515","year":2026},"citing_paper":{"arxiv_id":"2605.29295","last_updated":"2026-05-28T03:22:54Z","snapshot_observed_at":"2026-08-01T20:29:18.619041Z","submitted_at":"2026-05-28T03:22:54Z","title":"EvoGM: Learning to Merge LLMs via Evolutionary Generative Optimization","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-07-11T11:50:26.030339Z"},"links":{"cited_paper":"/paper/2604.22823","citing_paper":"/paper/2605.29295"},"observation_digest":"sha256:9afa12de2a591ea7f469dd8ddb56eefbd44e3396d56bb60bd2e05f80827bf935","observation_id":"16eea924-674c-4e63-945e-8710ea9910ae","resolution":{"observed_at":"2026-06-29T00:22:50.692442Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2604.22823/citation-record","integrity":"/paper/2604.22823/integrity","json":"/paper/2604.22823/citation-record.json","paper":"/paper/2604.22823"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T19:35:19.592168Z","title":"A brief survey on semantic segmentation with deep learning,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2604.22823","last_updated":"2026-07-13T15:11:18Z","snapshot_observed_at":"2026-07-16T23:19:34.740600Z","submitted_at":"2026-04-18T09:38:03Z","title":"PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-07-14T19:35:19.592168Z"},"links":{"citing_paper":"/paper/2604.22823"},"observation_digest":"sha256:4b1fe3c24fb4e02b97c9aec29d91f46a726a9d4c91bdc4ef23c19a122ea1e388","observation_id":"d6ec6856-665b-4018-84c1-d3f44e2e687f","resolution":{"observed_at":"2026-07-14T19:35:19.592168Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T19:35:19.592168Z","title":"Review the state-of-the-art technologies of se- mantic segmentation based on deep learning,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2604.22823","last_updated":"2026-07-13T15:11:18Z","snapshot_observed_at":"2026-07-16T23:19:34.740600Z","submitted_at":"2026-04-18T09:38:03Z","title":"PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-07-14T19:35:19.592168Z"},"links":{"citing_paper":"/paper/2604.22823"},"observation_digest":"sha256:83d9648ce855d5e0eea44c0fd4de983ae9f58ee034a3764e8661f25f5a9e8021","observation_id":"a377cf98-9022-4ec6-bd84-069994df82a7","resolution":{"observed_at":"2026-07-14T19:35:19.592168Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T19:35:19.592168Z","title":"Vision-based semantic segmentation in scene understanding for autonomous driving: Recent achievements, challenges, and outlooks,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2604.22823","last_updated":"2026-07-13T15:11:18Z","snapshot_observed_at":"2026-07-16T23:19:34.740600Z","submitted_at":"2026-04-18T09:38:03Z","title":"PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-07-14T19:35:19.592168Z"},"links":{"citing_paper":"/paper/2604.22823"},"observation_digest":"sha256:a39c3b152c1455a7226137effe70210dfe547191ff1aa579d107a051dd2ec68c","observation_id":"0652146c-d87c-4862-be43-a0f600638447","resolution":{"observed_at":"2026-07-14T19:35:19.592168Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T19:35:19.592168Z","title":"Semantic segmentation for self-driving cars us- ing deep learning: a survey,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2604.22823","last_updated":"2026-07-13T15:11:18Z","snapshot_observed_at":"2026-07-16T23:19:34.740600Z","submitted_at":"2026-04-18T09:38:03Z","title":"PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-07-14T19:35:19.592168Z"},"links":{"citing_paper":"/paper/2604.22823"},"observation_digest":"sha256:dd9173ac2062093e83a852a6c95182299cde1be274de44176a6ba30803d897bf","observation_id":"8ce1c4e3-c871-41bb-89ad-8b74bcdac29a","resolution":{"observed_at":"2026-07-14T19:35:19.592168Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T19:35:19.592168Z","title":"Real-time semantic image segmen- tation with deep learning for autonomous driving: A survey,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2604.22823","last_updated":"2026-07-13T15:11:18Z","snapshot_observed_at":"2026-07-16T23:19:34.740600Z","submitted_at":"2026-04-18T09:38:03Z","title":"PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-07-14T19:35:19.592168Z"},"links":{"citing_paper":"/paper/2604.22823"},"observation_digest":"sha256:6d8c2c80fec1a92e74e1c54b241e4036b574789f60fba84c00ffc2ac56cdcdc0","observation_id":"0e651d4b-16ac-458c-8035-67ec3f86f57d","resolution":{"observed_at":"2026-07-14T19:35:19.592168Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T19:35:19.592168Z","title":"View-coherent correlation consistency for semi-supervised se- mantic segmentation,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2604.22823","last_updated":"2026-07-13T15:11:18Z","snapshot_observed_at":"2026-07-16T23:19:34.740600Z","submitted_at":"2026-04-18T09:38:03Z","title":"PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-07-14T19:35:19.592168Z"},"links":{"citing_paper":"/paper/2604.22823"},"observation_digest":"sha256:675218e0b3195dcad750d08b3268b6228b69f428583980a28bbab648afa61ed4","observation_id":"fd131328-e261-4c92-bfb9-9a845a5acec0","resolution":{"observed_at":"2026-07-14T19:35:19.592168Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T19:35:19.592168Z","title":"Perturbed and strict mean teachers for semi-supervised semantic segmenta- tion,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2604.22823","last_updated":"2026-07-13T15:11:18Z","snapshot_observed_at":"2026-07-16T23:19:34.740600Z","submitted_at":"2026-04-18T09:38:03Z","title":"PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-07-14T19:35:19.592168Z"},"links":{"citing_paper":"/paper/2604.22823"},"observation_digest":"sha256:68f7718e4b76699cc901a8eb17e3209678c8ac3d16c95ac8cd0cd5dfe0ba107d","observation_id":"16e08bf8-7414-479b-97ad-30574922f5b0","resolution":{"observed_at":"2026-07-14T19:35:19.592168Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T19:35:19.592168Z","title":"Semi- supervised semantic segmentation using cross- consistency training for pavement crack detec- tion,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2604.22823","last_updated":"2026-07-13T15:11:18Z","snapshot_observed_at":"2026-07-16T23:19:34.740600Z","submitted_at":"2026-04-18T09:38:03Z","title":"PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-07-14T19:35:19.592168Z"},"links":{"citing_paper":"/paper/2604.22823"},"observation_digest":"sha256:c80ff492bcee2b07bce17eb0b6e82e2ac9da3f8c0b350d29402c150f9dfb320c","observation_id":"ff7cae82-8e74-4250-b18f-9239a07dd274","resolution":{"observed_at":"2026-07-14T19:35:19.592168Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T19:35:19.592168Z","title":"Semi- supervised semantic segmentation with cross- consistency training,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2604.22823","last_updated":"2026-07-13T15:11:18Z","snapshot_observed_at":"2026-07-16T23:19:34.740600Z","submitted_at":"2026-04-18T09:38:03Z","title":"PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-07-14T19:35:19.592168Z"},"links":{"citing_paper":"/paper/2604.22823"},"observation_digest":"sha256:308fcff964b95d3ef3f3c3d3afdf111a60f286e943fcdc66c5482c40d1c6eb99","observation_id":"8d5d62fa-318c-45d7-908b-182fdf116924","resolution":{"observed_at":"2026-07-14T19:35:19.592168Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T19:35:19.592168Z","title":"Semi-supervised semantic segmentation using unreliable pseudo- labels,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2604.22823","last_updated":"2026-07-13T15:11:18Z","snapshot_observed_at":"2026-07-16T23:19:34.740600Z","submitted_at":"2026-04-18T09:38:03Z","title":"PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-07-14T19:35:19.592168Z"},"links":{"citing_paper":"/paper/2604.22823"},"observation_digest":"sha256:015c6e5d63d05e64a0519498a04e0303588a8708cbe9c6bfd9b6dd272758ec95","observation_id":"b20ea112-ef19-4586-9bf7-4ad3039ff280","resolution":{"observed_at":"2026-07-14T19:35:19.592168Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T19:35:19.592168Z","title":"Learn- ing pseudo labels for semi-and-weakly super- vised semantic segmentation,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2604.22823","last_updated":"2026-07-13T15:11:18Z","snapshot_observed_at":"2026-07-16T23:19:34.740600Z","submitted_at":"2026-04-18T09:38:03Z","title":"PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-07-14T19:35:19.592168Z"},"links":{"citing_paper":"/paper/2604.22823"},"observation_digest":"sha256:bbdb96defb78ebcd4536a6ae33f453ed4ea1b63994acdd34b74790cd99b96116","observation_id":"51502568-65c9-4fb6-8266-0196d8abfe0c","resolution":{"observed_at":"2026-07-14T19:35:19.592168Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T19:35:19.592168Z","title":"Semi-supervised semantic segmentation via en- tropy minimization,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2604.22823","last_updated":"2026-07-13T15:11:18Z","snapshot_observed_at":"2026-07-16T23:19:34.740600Z","submitted_at":"2026-04-18T09:38:03Z","title":"PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-07-14T19:35:19.592168Z"},"links":{"citing_paper":"/paper/2604.22823"},"observation_digest":"sha256:e09b7f040aecd2712ca0ce155158f5006017c6bee2255c0d7fe3909a170d872e","observation_id":"38428c2e-098a-4a06-a4e6-178fe4c1340d","resolution":{"observed_at":"2026-07-14T19:35:19.592168Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T19:35:19.592168Z","title":"Semantic segmentation with genera- tive models: Semi-supervised learning and strong out-of-domain generalization,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2604.22823","last_updated":"2026-07-13T15:11:18Z","snapshot_observed_at":"2026-07-16T23:19:34.740600Z","submitted_at":"2026-04-18T09:38:03Z","title":"PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-07-14T19:35:19.592168Z"},"links":{"citing_paper":"/paper/2604.22823"},"observation_digest":"sha256:310e1917ee7d2a181d10bcb141e14462c013c54fc837eba2a49d9427384a4832","observation_id":"59981cb9-a332-4a17-8060-f0805442a026","resolution":{"observed_at":"2026-07-14T19:35:19.592168Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T19:35:19.592168Z","title":"Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning re- sults,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2604.22823","last_updated":"2026-07-13T15:11:18Z","snapshot_observed_at":"2026-07-16T23:19:34.740600Z","submitted_at":"2026-04-18T09:38:03Z","title":"PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-07-14T19:35:19.592168Z"},"links":{"citing_paper":"/paper/2604.22823"},"observation_digest":"sha256:ee100f076dfd185ad14a295e7174c2f922df9b04a513f90c2f6948aa8c90e4df","observation_id":"00249789-d866-4d39-8d5f-e7958979e87c","resolution":{"observed_at":"2026-07-14T19:35:19.592168Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T19:35:19.592168Z","title":"Semantic segmentation in autonomous driving—an example of fcn,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2604.22823","last_updated":"2026-07-13T15:11:18Z","snapshot_observed_at":"2026-07-16T23:19:34.740600Z","submitted_at":"2026-04-18T09:38:03Z","title":"PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-07-14T19:35:19.592168Z"},"links":{"citing_paper":"/paper/2604.22823"},"observation_digest":"sha256:fba5b5dde46e556bde1f14df110e55b446387fa4f33302bad98cdac212cc66b4","observation_id":"70a4c292-6149-4237-bcd5-c764f79a34d7","resolution":{"observed_at":"2026-07-14T19:35:19.592168Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T19:35:19.592168Z","title":"Segnet: A deep convolutional encoder-decoder architecture for image segmentation,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2604.22823","last_updated":"2026-07-13T15:11:18Z","snapshot_observed_at":"2026-07-16T23:19:34.740600Z","submitted_at":"2026-04-18T09:38:03Z","title":"PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-07-14T19:35:19.592168Z"},"links":{"citing_paper":"/paper/2604.22823"},"observation_digest":"sha256:d64cb31ea220ef206974e861343faeaa935dac94ad0296a6003806eb919f24fd","observation_id":"82f8943b-89ad-4f6d-b8d3-c421451c54d8","resolution":{"observed_at":"2026-07-14T19:35:19.592168Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T19:35:19.592168Z","title":"Image segmentation for self-driving car,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2604.22823","last_updated":"2026-07-13T15:11:18Z","snapshot_observed_at":"2026-07-16T23:19:34.740600Z","submitted_at":"2026-04-18T09:38:03Z","title":"PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-07-14T19:35:19.592168Z"},"links":{"citing_paper":"/paper/2604.22823"},"observation_digest":"sha256:06c52383e424dcebb47bd801b819e21d7ad5c61e4a9d84dc18cd7f8d9369264e","observation_id":"91f9f0bc-1ef9-4d3c-8387-9c5923ec4fc2","resolution":{"observed_at":"2026-07-14T19:35:19.592168Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T19:35:19.592168Z","title":"Two-stage framework with improved u-net based on self-supervised contrastive learning for pavement crack segmen- tation,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2604.22823","last_updated":"2026-07-13T15:11:18Z","snapshot_observed_at":"2026-07-16T23:19:34.740600Z","submitted_at":"2026-04-18T09:38:03Z","title":"PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-07-14T19:35:19.592168Z"},"links":{"citing_paper":"/paper/2604.22823"},"observation_digest":"sha256:e27101dc303f663700c5fcc74462a6a28ef2864d623a55bcd6d3e02b8ae36280","observation_id":"f8e987ed-c907-4a7b-ab0b-f11fcd337182","resolution":{"observed_at":"2026-07-14T19:35:19.592168Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T19:35:19.592168Z","title":"Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2604.22823","last_updated":"2026-07-13T15:11:18Z","snapshot_observed_at":"2026-07-16T23:19:34.740600Z","submitted_at":"2026-04-18T09:38:03Z","title":"PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-07-14T19:35:19.592168Z"},"links":{"citing_paper":"/paper/2604.22823"},"observation_digest":"sha256:2a3c98ae00a12625d77a49b8d11fd4618c0a0b07f7044b1ba758451ddee12af3","observation_id":"2347c346-81e8-4447-b4e2-584eb1e8eae0","resolution":{"observed_at":"2026-07-14T19:35:19.592168Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T19:35:19.592168Z","title":"Sats: Self-attention transfer for continual semantic segmentation,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2604.22823","last_updated":"2026-07-13T15:11:18Z","snapshot_observed_at":"2026-07-16T23:19:34.740600Z","submitted_at":"2026-04-18T09:38:03Z","title":"PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-07-14T19:35:19.592168Z"},"links":{"citing_paper":"/paper/2604.22823"},"observation_digest":"sha256:7e5d08ddea5a8a3653fa7794c3fdd3f2f0917d1e9311c967ebe356e0869d09a5","observation_id":"82dd7cfb-7623-4ffc-8e98-e29914f03396","resolution":{"observed_at":"2026-07-14T19:35:19.592168Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T19:35:19.592168Z","title":"Scribblenet: Efficient interactive anno- tation of urban city scenes for semantic segmen- tation,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2604.22823","last_updated":"2026-07-13T15:11:18Z","snapshot_observed_at":"2026-07-16T23:19:34.740600Z","submitted_at":"2026-04-18T09:38:03Z","title":"PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-07-14T19:35:19.592168Z"},"links":{"citing_paper":"/paper/2604.22823"},"observation_digest":"sha256:42e5de6aba8c5c1086cd5fbc58d63ae6f1e733856836ba05d340b6a0aa0ebdc7","observation_id":"d1c78840-8e11-447b-8087-f5a5f92d8f6b","resolution":{"observed_at":"2026-07-14T19:35:19.592168Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T19:35:19.592168Z","title":"Simpler is better: Few-shot semantic segmentation with classifier weight transformer,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2604.22823","last_updated":"2026-07-13T15:11:18Z","snapshot_observed_at":"2026-07-16T23:19:34.740600Z","submitted_at":"2026-04-18T09:38:03Z","title":"PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-07-14T19:35:19.592168Z"},"links":{"citing_paper":"/paper/2604.22823"},"observation_digest":"sha256:13b29593f6087080c25c99f612b6fafb8e3fe29bcb2bae2a6d705c9b86cab974","observation_id":"e77fdb1d-3771-4207-ba3e-5111ce839a0f","resolution":{"observed_at":"2026-07-14T19:35:19.592168Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T19:35:19.592168Z","title":"Beyond low- dimensional features: Enhancing semi-supervised medical image semantic segmentation with ad- vanced consistency learning techniques,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2604.22823","last_updated":"2026-07-13T15:11:18Z","snapshot_observed_at":"2026-07-16T23:19:34.740600Z","submitted_at":"2026-04-18T09:38:03Z","title":"PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-07-14T19:35:19.592168Z"},"links":{"citing_paper":"/paper/2604.22823"},"observation_digest":"sha256:4bf1d681ba618a44b14af41ae18c95bfe75b85d44c95dae62e2b6ee692a9e747","observation_id":"23a75819-5db6-40e3-b676-a53fea6ee50f","resolution":{"observed_at":"2026-07-14T19:35:19.592168Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T19:35:19.592168Z","title":"Semi-supervised semantic segmentation with prototype-based consistency regularization,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2604.22823","last_updated":"2026-07-13T15:11:18Z","snapshot_observed_at":"2026-07-16T23:19:34.740600Z","submitted_at":"2026-04-18T09:38:03Z","title":"PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-07-14T19:35:19.592168Z"},"links":{"citing_paper":"/paper/2604.22823"},"observation_digest":"sha256:855ab913ff00eddf01207aceb829c0e075d99e7a1e84912c39fee9edd720a1b4","observation_id":"1e4fd3bb-80b7-4d4f-ab17-c082abe27ed0","resolution":{"observed_at":"2026-07-14T19:35:19.592168Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T19:35:19.592168Z","title":"Enhancing semi-supervised semantic segmen- tation of remote sensing images via fea- ture perturbation-based consistency regulariza- tion methods,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2604.22823","last_updated":"2026-07-13T15:11:18Z","snapshot_observed_at":"2026-07-16T23:19:34.740600Z","submitted_at":"2026-04-18T09:38:03Z","title":"PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-07-14T19:35:19.592168Z"},"links":{"citing_paper":"/paper/2604.22823"},"observation_digest":"sha256:890fb13f92e5f17ec7eb056dcce4c37ce5dcc0554659430444715fdf703af2bd","observation_id":"356373bb-4e1c-4d52-8df5-da9189307563","resolution":{"observed_at":"2026-07-14T19:35:19.592168Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T19:35:19.592168Z","title":"Improving semi-supervised and domain-adaptive semantic segmentation with self-supervised depth estimation,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2604.22823","last_updated":"2026-07-13T15:11:18Z","snapshot_observed_at":"2026-07-16T23:19:34.740600Z","submitted_at":"2026-04-18T09:38:03Z","title":"PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-07-14T19:35:19.592168Z"},"links":{"citing_paper":"/paper/2604.22823"},"observation_digest":"sha256:cfa9d0b5988864554edbc54aa778c9cc98dc1e79ea8c4259fbb197b323a4db4a","observation_id":"6aef27a3-5a9b-4b6e-8774-6a5fc292d5fb","resolution":{"observed_at":"2026-07-14T19:35:19.592168Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T19:35:19.592168Z","title":"Learning from pixel-level label noise: A new perspective for semi-supervised semantic seg- mentation,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2604.22823","last_updated":"2026-07-13T15:11:18Z","snapshot_observed_at":"2026-07-16T23:19:34.740600Z","submitted_at":"2026-04-18T09:38:03Z","title":"PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-07-14T19:35:19.592168Z"},"links":{"citing_paper":"/paper/2604.22823"},"observation_digest":"sha256:57a37cf5e4606456f59ac697b0a0862d0bc3714fd5611ac2a3605d2522e4ce81","observation_id":"fbbe91a4-0bfd-479e-ba63-82a2b4814e5d","resolution":{"observed_at":"2026-07-14T19:35:19.592168Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T19:35:19.592168Z","title":"Semi-supervised remote sensing image semantic segmentation via consistency regular- ization and average update of pseudo-label,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2604.22823","last_updated":"2026-07-13T15:11:18Z","snapshot_observed_at":"2026-07-16T23:19:34.740600Z","submitted_at":"2026-04-18T09:38:03Z","title":"PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-07-14T19:35:19.592168Z"},"links":{"citing_paper":"/paper/2604.22823"},"observation_digest":"sha256:8c183ed7751bd6da45a207d1f559fe7f4ef81e62026d08bf4dd12749b1681563","observation_id":"3c8bf0b7-7dfa-44c8-a9d5-8074d14c523f","resolution":{"observed_at":"2026-07-14T19:35:19.592168Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T19:35:19.592168Z","title":"Ambiguity-selective con- sistency regularization for mean-teacher semi- supervised medical image segmentation,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2604.22823","last_updated":"2026-07-13T15:11:18Z","snapshot_observed_at":"2026-07-16T23:19:34.740600Z","submitted_at":"2026-04-18T09:38:03Z","title":"PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-07-14T19:35:19.592168Z"},"links":{"citing_paper":"/paper/2604.22823"},"observation_digest":"sha256:79e6af03452ebcd37fc801e50be7bd6e6cdd761eddc93d64c68b55dd066d79b3","observation_id":"5ec6877b-55b9-498f-8f5f-2c4dd0701bf6","resolution":{"observed_at":"2026-07-14T19:35:19.592168Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T19:35:19.592168Z","title":"Dual attention based uncertainty-aware mean teacher model for semi-supervised cardiac image segmentation,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2604.22823","last_updated":"2026-07-13T15:11:18Z","snapshot_observed_at":"2026-07-16T23:19:34.740600Z","submitted_at":"2026-04-18T09:38:03Z","title":"PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-07-14T19:35:19.592168Z"},"links":{"citing_paper":"/paper/2604.22823"},"observation_digest":"sha256:3bedf7cdb5cf8a31fd9ef7099b75408ed0a64b6fde693c09ff505f47d57b777b","observation_id":"42777416-8600-493b-b060-45ae1a12925a","resolution":{"observed_at":"2026-07-14T19:35:19.592168Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T19:35:19.592168Z","title":"Semi-supervised brain lesion segmentation with an adapted mean teacher model,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2604.22823","last_updated":"2026-07-13T15:11:18Z","snapshot_observed_at":"2026-07-16T23:19:34.740600Z","submitted_at":"2026-04-18T09:38:03Z","title":"PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-07-14T19:35:19.592168Z"},"links":{"citing_paper":"/paper/2604.22823"},"observation_digest":"sha256:8bebc91a28b04446a1b105e38b8ab68d7c5576ba27fffe714d185663afbf1332","observation_id":"6b5d7a9a-848d-4aae-90a9-784104dd26ca","resolution":{"observed_at":"2026-07-14T19:35:19.592168Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T19:35:19.592168Z","title":"Semi-supervised semantic segmen- tation with cross teacher training,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2604.22823","last_updated":"2026-07-13T15:11:18Z","snapshot_observed_at":"2026-07-16T23:19:34.740600Z","submitted_at":"2026-04-18T09:38:03Z","title":"PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-07-14T19:35:19.592168Z"},"links":{"citing_paper":"/paper/2604.22823"},"observation_digest":"sha256:5739f50ae2da11e324ed5b08734d836c0266366844a6f1f34ebdc007ef2698c1","observation_id":"71a3411f-ae2b-450c-8a7f-9a8007778a92","resolution":{"observed_at":"2026-07-14T19:35:19.592168Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T19:35:19.592168Z","title":"Semi-supervised semantic segmentation via gentle teaching assis- tant,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2604.22823","last_updated":"2026-07-13T15:11:18Z","snapshot_observed_at":"2026-07-16T23:19:34.740600Z","submitted_at":"2026-04-18T09:38:03Z","title":"PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-07-14T19:35:19.592168Z"},"links":{"citing_paper":"/paper/2604.22823"},"observation_digest":"sha256:d877d6952cfe4c8980b4f0bbe796b5c5a65aeec67df73b2946d16ce5f8008a2b","observation_id":"5de8a950-cf5e-4665-aa9d-251b0742b62d","resolution":{"observed_at":"2026-07-14T19:35:19.592168Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T19:35:19.592168Z","title":"Automated evaluation of semantic segmentation robustness for autonomous driving,","venue":null,"work_id":null,"year":1951},"citing_paper":{"arxiv_id":"2604.22823","last_updated":"2026-07-13T15:11:18Z","snapshot_observed_at":"2026-07-16T23:19:34.740600Z","submitted_at":"2026-04-18T09:38:03Z","title":"PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-07-14T19:35:19.592168Z"},"links":{"citing_paper":"/paper/2604.22823"},"observation_digest":"sha256:3ddbb03a24f28ec1782b8fc329ca9a0b4f6400fb50f4809985506cae7e1534c1","observation_id":"eee7d296-c075-4a04-a706-cbc90a48e1a9","resolution":{"observed_at":"2026-07-14T19:35:19.592168Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T19:35:19.592168Z","title":"Deep cluster- ing for weakly-supervised semantic segmentation in autonomous driving scenes,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2604.22823","last_updated":"2026-07-13T15:11:18Z","snapshot_observed_at":"2026-07-16T23:19:34.740600Z","submitted_at":"2026-04-18T09:38:03Z","title":"PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-07-14T19:35:19.592168Z"},"links":{"citing_paper":"/paper/2604.22823"},"observation_digest":"sha256:bab244e4de3be8c4eea02872c97fdac6976ec4be2e939df47cf3ccd4dc0a8c29","observation_id":"dac15e81-4be5-4333-b661-168baaa8e202","resolution":{"observed_at":"2026-07-14T19:35:19.592168Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T19:35:19.592168Z","title":"Acdc: The adverse conditions dataset with correspon- dences for semantic driving scene understand- ing,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2604.22823","last_updated":"2026-07-13T15:11:18Z","snapshot_observed_at":"2026-07-16T23:19:34.740600Z","submitted_at":"2026-04-18T09:38:03Z","title":"PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-07-14T19:35:19.592168Z"},"links":{"citing_paper":"/paper/2604.22823"},"observation_digest":"sha256:08606bd6312ed193352a2bcf13fccadfbf758e8e94a2c750e52f1a4b75d763dd","observation_id":"147627bc-6430-4fe8-8b91-74b08dcf6491","resolution":{"observed_at":"2026-07-14T19:35:19.592168Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T19:35:19.592168Z","title":"Rainy wcity: A real rainfall dataset with diverse conditions for semantic driv- ing scene understanding","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2604.22823","last_updated":"2026-07-13T15:11:18Z","snapshot_observed_at":"2026-07-16T23:19:34.740600Z","submitted_at":"2026-04-18T09:38:03Z","title":"PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-07-14T19:35:19.592168Z"},"links":{"citing_paper":"/paper/2604.22823"},"observation_digest":"sha256:a7f0cbb97f686599a1b93a8290068bcf7adc6af7c78ae988f11e3ccac4268015","observation_id":"02765730-ffde-41c0-b593-b2b5e794f867","resolution":{"observed_at":"2026-07-14T19:35:19.592168Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T19:35:19.592168Z","title":"Revisiting weak-to-strong consistency in semi- supervised semantic segmentation,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2604.22823","last_updated":"2026-07-13T15:11:18Z","snapshot_observed_at":"2026-07-16T23:19:34.740600Z","submitted_at":"2026-04-18T09:38:03Z","title":"PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-07-14T19:35:19.592168Z"},"links":{"citing_paper":"/paper/2604.22823"},"observation_digest":"sha256:dc38e5b443c342a347430458b678bfdc1df86affd08981f4b2ce3fc7c1e907a3","observation_id":"b057c5aa-3a23-4bb9-a45b-6e08152acbb3","resolution":{"observed_at":"2026-07-14T19:35:19.592168Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T19:35:19.592168Z","title":"Semi-supervised semantic segmen- tation via adaptive equalization learning,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2604.22823","last_updated":"2026-07-13T15:11:18Z","snapshot_observed_at":"2026-07-16T23:19:34.740600Z","submitted_at":"2026-04-18T09:38:03Z","title":"PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-07-14T19:35:19.592168Z"},"links":{"citing_paper":"/paper/2604.22823"},"observation_digest":"sha256:3b1a9ece23bd7800a6676451a2f2b62059cef1b8428a2f25bada02a1c490189a","observation_id":"6f757aad-f54e-425c-9dd4-912a608581b4","resolution":{"observed_at":"2026-07-14T19:35:19.592168Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T19:35:19.592168Z","title":"Semi-supervised semantic segmentation with cross pseudo supervision,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2604.22823","last_updated":"2026-07-13T15:11:18Z","snapshot_observed_at":"2026-07-16T23:19:34.740600Z","submitted_at":"2026-04-18T09:38:03Z","title":"PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging","version":2},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-07-14T19:35:19.592168Z"},"links":{"citing_paper":"/paper/2604.22823"},"observation_digest":"sha256:44c0b5a07556a8022ab051120d042133909277c4107b266afcd52ef69d08e680","observation_id":"b8b03bbd-7b2e-4898-a145-c5056af14a4e","resolution":{"observed_at":"2026-07-14T19:35:19.592168Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T19:35:19.592168Z","title":"En- hanced soft label for semi-supervised semantic segmentation,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2604.22823","last_updated":"2026-07-13T15:11:18Z","snapshot_observed_at":"2026-07-16T23:19:34.740600Z","submitted_at":"2026-04-18T09:38:03Z","title":"PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging","version":2},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-07-14T19:35:19.592168Z"},"links":{"citing_paper":"/paper/2604.22823"},"observation_digest":"sha256:3e22b9b97cfefe77b1ab83f44fe0ffa5d01d681bfcca330c9dbedf72e1d2b851","observation_id":"ac0da051-71b2-4ecb-b73d-924f5b2e959d","resolution":{"observed_at":"2026-07-14T19:35:19.592168Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T19:35:19.592168Z","title":"The cityscapes dataset for se- mantic urban scene understanding,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2604.22823","last_updated":"2026-07-13T15:11:18Z","snapshot_observed_at":"2026-07-16T23:19:34.740600Z","submitted_at":"2026-04-18T09:38:03Z","title":"PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging","version":2},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-07-14T19:35:19.592168Z"},"links":{"citing_paper":"/paper/2604.22823"},"observation_digest":"sha256:9f67a6df55d41f03fc5e6989167ebf7f18bf97314fb21c8ec6ddfb8d17f886be","observation_id":"2d74ddbb-efe3-453d-8f64-453bda610068","resolution":{"observed_at":"2026-07-14T19:35:19.592168Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T19:35:19.592168Z","title":"The pascal vi- sual object classes (voc) challenge,","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2604.22823","last_updated":"2026-07-13T15:11:18Z","snapshot_observed_at":"2026-07-16T23:19:34.740600Z","submitted_at":"2026-04-18T09:38:03Z","title":"PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging","version":2},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-07-14T19:35:19.592168Z"},"links":{"citing_paper":"/paper/2604.22823"},"observation_digest":"sha256:d92a268662311d20a40693fef181575b67300c56c23e53069b9eea3f27b4f535","observation_id":"a3415aa9-28d7-46ac-9b64-bcbb665cab26","resolution":{"observed_at":"2026-07-14T19:35:19.592168Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T19:35:19.592168Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2604.22823","last_updated":"2026-07-13T15:11:18Z","snapshot_observed_at":"2026-07-16T23:19:34.740600Z","submitted_at":"2026-04-18T09:38:03Z","title":"PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging","version":2},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-07-14T19:35:19.592168Z"},"links":{"citing_paper":"/paper/2604.22823"},"observation_digest":"sha256:81d9fa1126f1ac59b2a7a5f3a9068dcd937fd877dad5c77b1ed85bb7569d3e98","observation_id":"8321de08-b2cf-4054-aa8d-129b85c6008f","resolution":{"observed_at":"2026-07-14T19:35:19.592168Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T19:35:19.592168Z","title":"Semi-supervised seman- tic segmentation needs strong, high-dimensional perturbations,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2604.22823","last_updated":"2026-07-13T15:11:18Z","snapshot_observed_at":"2026-07-16T23:19:34.740600Z","submitted_at":"2026-04-18T09:38:03Z","title":"PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging","version":2},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-07-14T19:35:19.592168Z"},"links":{"citing_paper":"/paper/2604.22823"},"observation_digest":"sha256:c5423e10203ce3212cc5ad7580b9f43ace7e83bfd6c69204eae5abbf34edfb2d","observation_id":"15e58c2f-2f0c-4ef7-9623-7262f9e63b83","resolution":{"observed_at":"2026-07-14T19:35:19.592168Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T19:35:19.592168Z","title":"Semi-supervised semantic segmentation using unreliable pseudo- labels,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2604.22823","last_updated":"2026-07-13T15:11:18Z","snapshot_observed_at":"2026-07-16T23:19:34.740600Z","submitted_at":"2026-04-18T09:38:03Z","title":"PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging","version":2},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-07-14T19:35:19.592168Z"},"links":{"citing_paper":"/paper/2604.22823"},"observation_digest":"sha256:16b56208d46ad27507dfb45c54324ccf1617ed461e0db1e65cdcf411a23f4a27","observation_id":"eb1d719d-ca26-4365-8824-6b6c802718b3","resolution":{"observed_at":"2026-07-14T19:35:19.592168Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T19:35:19.592168Z","title":"Semi-supervised seman- tic segmentation with error localization network,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2604.22823","last_updated":"2026-07-13T15:11:18Z","snapshot_observed_at":"2026-07-16T23:19:34.740600Z","submitted_at":"2026-04-18T09:38:03Z","title":"PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging","version":2},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-07-14T19:35:19.592168Z"},"links":{"citing_paper":"/paper/2604.22823"},"observation_digest":"sha256:2df837568c20a37a82265c6a7fe853c6461ea9afbafa662ebc1fc3dd4bfc8f0f","observation_id":"6fe17ad7-4b5f-43b5-851b-baf43aed822d","resolution":{"observed_at":"2026-07-14T19:35:19.592168Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T19:35:19.592168Z","title":"Augmentation matters: A simple-yet- effective approach to semi-supervised semantic segmentation,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2604.22823","last_updated":"2026-07-13T15:11:18Z","snapshot_observed_at":"2026-07-16T23:19:34.740600Z","submitted_at":"2026-04-18T09:38:03Z","title":"PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging","version":2},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-07-14T19:35:19.592168Z"},"links":{"citing_paper":"/paper/2604.22823"},"observation_digest":"sha256:2656e91e87005dc40bfba3940e06b54bada2f3da33664207960058b13544fa85","observation_id":"5d380791-26a5-4a71-96a4-6788febc1535","resolution":{"observed_at":"2026-07-14T19:35:19.592168Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T19:35:19.592168Z","title":"His research interests include intelli- gent agents, decision making, social net- works, and computer games","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2604.22823","last_updated":"2026-07-13T15:11:18Z","snapshot_observed_at":"2026-07-16T23:19:34.740600Z","submitted_at":"2026-04-18T09:38:03Z","title":"PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging","version":2},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-07-14T19:35:19.592168Z"},"links":{"citing_paper":"/paper/2604.22823"},"observation_digest":"sha256:7984192b2701872fff82172c360565951eba5797ff38f4848050427aecba4b9c","observation_id":"614de962-9793-4a5d-9d28-4db3b0490934","resolution":{"observed_at":"2026-07-14T19:35:19.592168Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2604.22823","last_updated":"2026-07-13T15:11:18Z","latest_version":2,"primary_category":"cs.CV","snapshot_observed_at":"2026-07-16T23:19:34.740600Z","submitted_at":"2026-04-18T09:38:03Z","title":"PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging"},"reference_resolution":{"displayed":49,"state_counts":{"malformed_identifier":1,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":48,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":49},"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":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"thesis":"As of 6 August 2026, this Paper Citation Record lists 49 of 49 outbound references and 1 inbound Pith citation observation for arXiv:2604.22823."}