{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:UAHFMIZRR3RLQVURXIMFBTR2VR","short_pith_number":"pith:UAHFMIZR","canonical_record":{"source":{"id":"2207.13600","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-07-27T16:00:28Z","cross_cats_sorted":[],"title_canon_sha256":"da89f6ed3586cfd0e7a9b50a66dc06138fb6ce46b7e80576114811ca9b0b509d","abstract_canon_sha256":"ea87ca7fd9a2e642d74af7d12a60c2ae70aa710f88b489efcc3bfcbc108e454e"},"schema_version":"1.0"},"canonical_sha256":"a00e5623318ee2b85691ba1850ce3aac6c20b3179974f2f175e65f3c55825f6a","source":{"kind":"arxiv","id":"2207.13600","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2207.13600","created_at":"2026-07-05T04:44:08Z"},{"alias_kind":"arxiv_version","alias_value":"2207.13600v1","created_at":"2026-07-05T04:44:08Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2207.13600","created_at":"2026-07-05T04:44:08Z"},{"alias_kind":"pith_short_12","alias_value":"UAHFMIZRR3RL","created_at":"2026-07-05T04:44:08Z"},{"alias_kind":"pith_short_16","alias_value":"UAHFMIZRR3RLQVUR","created_at":"2026-07-05T04:44:08Z"},{"alias_kind":"pith_short_8","alias_value":"UAHFMIZR","created_at":"2026-07-05T04:44:08Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:UAHFMIZRR3RLQVURXIMFBTR2VR","target":"record","payload":{"canonical_record":{"source":{"id":"2207.13600","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-07-27T16:00:28Z","cross_cats_sorted":[],"title_canon_sha256":"da89f6ed3586cfd0e7a9b50a66dc06138fb6ce46b7e80576114811ca9b0b509d","abstract_canon_sha256":"ea87ca7fd9a2e642d74af7d12a60c2ae70aa710f88b489efcc3bfcbc108e454e"},"schema_version":"1.0"},"canonical_sha256":"a00e5623318ee2b85691ba1850ce3aac6c20b3179974f2f175e65f3c55825f6a","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:44:08.995049Z","signature_b64":"aqq4/byAmaziSLy72tMnsUcnsNyENLz8IYtVh5Y11ZbMzv23e7VQs9pU1M7Kn7juNYq0xjG9cp9DoPI4pMnoBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a00e5623318ee2b85691ba1850ce3aac6c20b3179974f2f175e65f3c55825f6a","last_reissued_at":"2026-07-05T04:44:08.994594Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:44:08.994594Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2207.13600","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T04:44:08Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Hag6KFnkgefs3gPEUUvUB3DGTPJ7fopgtKSwovHoh9G3FIqmWB2dMBJxfYKJBcIJGv7q2duacfjr/21kl9TEBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T00:52:40.539050Z"},"content_sha256":"878efcbde1bd3b5ce525e8cc6f8bd01949199feb01a73d9023ad9dd51e77e675","schema_version":"1.0","event_id":"sha256:878efcbde1bd3b5ce525e8cc6f8bd01949199feb01a73d9023ad9dd51e77e675"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:UAHFMIZRR3RLQVURXIMFBTR2VR","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Lightweight and Progressively-Scalable Networks for Semantic Segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Tao Mei, Ting Yao, Yiheng Zhang, Zhaofan Qiu","submitted_at":"2022-07-27T16:00:28Z","abstract_excerpt":"Multi-scale learning frameworks have been regarded as a capable class of models to boost semantic segmentation. The problem nevertheless is not trivial especially for the real-world deployments, which often demand high efficiency in inference latency. In this paper, we thoroughly analyze the design of convolutional blocks (the type of convolutions and the number of channels in convolutions), and the ways of interactions across multiple scales, all from lightweight standpoint for semantic segmentation. With such in-depth comparisons, we conclude three principles, and accordingly devise Lightwei"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2207.13600","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2207.13600/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T04:44:08Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"bFl1ulAFReJDteNQkVg6NXNL83NSwJJwRZDowR7IEm0hHwT5tWQEim/c9WCskvDetV4jEYz45rp9mLNPLBEAAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T00:52:40.539539Z"},"content_sha256":"458eaa3c2670f1a2ece3cee0614fd6cb3fc545cf94c17a22e3bbde799a9a2201","schema_version":"1.0","event_id":"sha256:458eaa3c2670f1a2ece3cee0614fd6cb3fc545cf94c17a22e3bbde799a9a2201"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/UAHFMIZRR3RLQVURXIMFBTR2VR/bundle.json","state_url":"https://pith.science/pith/UAHFMIZRR3RLQVURXIMFBTR2VR/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/UAHFMIZRR3RLQVURXIMFBTR2VR/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-07T00:52:40Z","links":{"resolver":"https://pith.science/pith/UAHFMIZRR3RLQVURXIMFBTR2VR","bundle":"https://pith.science/pith/UAHFMIZRR3RLQVURXIMFBTR2VR/bundle.json","state":"https://pith.science/pith/UAHFMIZRR3RLQVURXIMFBTR2VR/state.json","well_known_bundle":"https://pith.science/.well-known/pith/UAHFMIZRR3RLQVURXIMFBTR2VR/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:UAHFMIZRR3RLQVURXIMFBTR2VR","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"ea87ca7fd9a2e642d74af7d12a60c2ae70aa710f88b489efcc3bfcbc108e454e","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-07-27T16:00:28Z","title_canon_sha256":"da89f6ed3586cfd0e7a9b50a66dc06138fb6ce46b7e80576114811ca9b0b509d"},"schema_version":"1.0","source":{"id":"2207.13600","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2207.13600","created_at":"2026-07-05T04:44:08Z"},{"alias_kind":"arxiv_version","alias_value":"2207.13600v1","created_at":"2026-07-05T04:44:08Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2207.13600","created_at":"2026-07-05T04:44:08Z"},{"alias_kind":"pith_short_12","alias_value":"UAHFMIZRR3RL","created_at":"2026-07-05T04:44:08Z"},{"alias_kind":"pith_short_16","alias_value":"UAHFMIZRR3RLQVUR","created_at":"2026-07-05T04:44:08Z"},{"alias_kind":"pith_short_8","alias_value":"UAHFMIZR","created_at":"2026-07-05T04:44:08Z"}],"graph_snapshots":[{"event_id":"sha256:458eaa3c2670f1a2ece3cee0614fd6cb3fc545cf94c17a22e3bbde799a9a2201","target":"graph","created_at":"2026-07-05T04:44:08Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2207.13600/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Multi-scale learning frameworks have been regarded as a capable class of models to boost semantic segmentation. The problem nevertheless is not trivial especially for the real-world deployments, which often demand high efficiency in inference latency. In this paper, we thoroughly analyze the design of convolutional blocks (the type of convolutions and the number of channels in convolutions), and the ways of interactions across multiple scales, all from lightweight standpoint for semantic segmentation. With such in-depth comparisons, we conclude three principles, and accordingly devise Lightwei","authors_text":"Tao Mei, Ting Yao, Yiheng Zhang, Zhaofan Qiu","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-07-27T16:00:28Z","title":"Lightweight and Progressively-Scalable Networks for Semantic Segmentation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2207.13600","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:878efcbde1bd3b5ce525e8cc6f8bd01949199feb01a73d9023ad9dd51e77e675","target":"record","created_at":"2026-07-05T04:44:08Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"ea87ca7fd9a2e642d74af7d12a60c2ae70aa710f88b489efcc3bfcbc108e454e","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-07-27T16:00:28Z","title_canon_sha256":"da89f6ed3586cfd0e7a9b50a66dc06138fb6ce46b7e80576114811ca9b0b509d"},"schema_version":"1.0","source":{"id":"2207.13600","kind":"arxiv","version":1}},"canonical_sha256":"a00e5623318ee2b85691ba1850ce3aac6c20b3179974f2f175e65f3c55825f6a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a00e5623318ee2b85691ba1850ce3aac6c20b3179974f2f175e65f3c55825f6a","first_computed_at":"2026-07-05T04:44:08.994594Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:44:08.994594Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"aqq4/byAmaziSLy72tMnsUcnsNyENLz8IYtVh5Y11ZbMzv23e7VQs9pU1M7Kn7juNYq0xjG9cp9DoPI4pMnoBA==","signature_status":"signed_v1","signed_at":"2026-07-05T04:44:08.995049Z","signed_message":"canonical_sha256_bytes"},"source_id":"2207.13600","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:878efcbde1bd3b5ce525e8cc6f8bd01949199feb01a73d9023ad9dd51e77e675","sha256:458eaa3c2670f1a2ece3cee0614fd6cb3fc545cf94c17a22e3bbde799a9a2201"],"state_sha256":"9b1705044bc134c9634c08a3e9cd367408ce5b66b48ac13569ce03960683afa3"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"zIwkvtJzlK+0bub8K/0IAAp2NqWGTOfumExViWdhtyCKAsx4us2g4WHIK31sty8bSAyRQakSmOfrzrmXlFBEBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-07T00:52:40.544013Z","bundle_sha256":"b930bc7b4bcd9195e5df7b94941485d815d816f0f87f01119e7b5566f13e265a"}}