{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:BPYWA2IVSKKYNYHTZSJPDACHRW","short_pith_number":"pith:BPYWA2IV","canonical_record":{"source":{"id":"2210.14530","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2022-10-26T07:42:34Z","cross_cats_sorted":[],"title_canon_sha256":"b7f3ed42bc64971dd7bff6adcfd5019c9f02f5f2ee25323cb43b7c184d45409d","abstract_canon_sha256":"f48425b3fa5032eb6172c781fe5545fd704d3b0ed710a660b8c3fcc627db5645"},"schema_version":"1.0"},"canonical_sha256":"0bf1606915929586e0f3cc92f180478daf78a2637b5c9bf8868e057c29c8bea0","source":{"kind":"arxiv","id":"2210.14530","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2210.14530","created_at":"2026-07-05T05:10:46Z"},{"alias_kind":"arxiv_version","alias_value":"2210.14530v1","created_at":"2026-07-05T05:10:46Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2210.14530","created_at":"2026-07-05T05:10:46Z"},{"alias_kind":"pith_short_12","alias_value":"BPYWA2IVSKKY","created_at":"2026-07-05T05:10:46Z"},{"alias_kind":"pith_short_16","alias_value":"BPYWA2IVSKKYNYHT","created_at":"2026-07-05T05:10:46Z"},{"alias_kind":"pith_short_8","alias_value":"BPYWA2IV","created_at":"2026-07-05T05:10:46Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:BPYWA2IVSKKYNYHTZSJPDACHRW","target":"record","payload":{"canonical_record":{"source":{"id":"2210.14530","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2022-10-26T07:42:34Z","cross_cats_sorted":[],"title_canon_sha256":"b7f3ed42bc64971dd7bff6adcfd5019c9f02f5f2ee25323cb43b7c184d45409d","abstract_canon_sha256":"f48425b3fa5032eb6172c781fe5545fd704d3b0ed710a660b8c3fcc627db5645"},"schema_version":"1.0"},"canonical_sha256":"0bf1606915929586e0f3cc92f180478daf78a2637b5c9bf8868e057c29c8bea0","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:10:46.462842Z","signature_b64":"cuGDfXQ0EXXHq1A4IJ4CmJNhkqp1cdhPu/qGrkbVKJ7ms/SrhocfjF6VM1k5ECaseZoU99Y2J3IvD+poId5JAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0bf1606915929586e0f3cc92f180478daf78a2637b5c9bf8868e057c29c8bea0","last_reissued_at":"2026-07-05T05:10:46.462451Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:10:46.462451Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2210.14530","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":"2026-07-05T05:10:46Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"MPOqv030vmiAhohbHEGI/cLuZoA1SWduQTOwpaWDoF0K5K7TOAsuxY5FCgOYrgrVsFVdPNV8KuVe71gXOh6fCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T05:49:19.372222Z"},"content_sha256":"db6e1ef259973b9019f85778ba21b48bee35bacb46c52f8a205cccafee04b85e","schema_version":"1.0","event_id":"sha256:db6e1ef259973b9019f85778ba21b48bee35bacb46c52f8a205cccafee04b85e"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:BPYWA2IVSKKYNYHTZSJPDACHRW","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"RGB-T Semantic Segmentation with Location, Activation, and Sharpening","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Dan Zeng, Gongyang Li, Xinpeng Zhang, Yike Wang, Zhi Liu","submitted_at":"2022-10-26T07:42:34Z","abstract_excerpt":"Semantic segmentation is important for scene understanding. To address the scenes of adverse illumination conditions of natural images, thermal infrared (TIR) images are introduced. Most existing RGB-T semantic segmentation methods follow three cross-modal fusion paradigms, i.e. encoder fusion, decoder fusion, and feature fusion. Some methods, unfortunately, ignore the properties of RGB and TIR features or the properties of features at different levels. In this paper, we propose a novel feature fusion-based network for RGB-T semantic segmentation, named \\emph{LASNet}, which follows three steps"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2210.14530","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/2210.14530/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":"2026-07-05T05:10:46Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"kzIDRdo2/2Yf9BmYdAhCKpz7rwwhKdC7ksgYwLm5FFvcDRPBrp4ghHnml5EAGwjp7VJJt0d65tB7rVtm/8MjDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T05:49:19.372623Z"},"content_sha256":"36a314444a914ea0940843b453ec074d2b1cf3667e741fd045cd7b2b30bfc810","schema_version":"1.0","event_id":"sha256:36a314444a914ea0940843b453ec074d2b1cf3667e741fd045cd7b2b30bfc810"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/BPYWA2IVSKKYNYHTZSJPDACHRW/bundle.json","state_url":"https://pith.science/pith/BPYWA2IVSKKYNYHTZSJPDACHRW/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/BPYWA2IVSKKYNYHTZSJPDACHRW/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":"2026-07-07T05:49:19Z","links":{"resolver":"https://pith.science/pith/BPYWA2IVSKKYNYHTZSJPDACHRW","bundle":"https://pith.science/pith/BPYWA2IVSKKYNYHTZSJPDACHRW/bundle.json","state":"https://pith.science/pith/BPYWA2IVSKKYNYHTZSJPDACHRW/state.json","well_known_bundle":"https://pith.science/.well-known/pith/BPYWA2IVSKKYNYHTZSJPDACHRW/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:BPYWA2IVSKKYNYHTZSJPDACHRW","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":"f48425b3fa5032eb6172c781fe5545fd704d3b0ed710a660b8c3fcc627db5645","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2022-10-26T07:42:34Z","title_canon_sha256":"b7f3ed42bc64971dd7bff6adcfd5019c9f02f5f2ee25323cb43b7c184d45409d"},"schema_version":"1.0","source":{"id":"2210.14530","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2210.14530","created_at":"2026-07-05T05:10:46Z"},{"alias_kind":"arxiv_version","alias_value":"2210.14530v1","created_at":"2026-07-05T05:10:46Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2210.14530","created_at":"2026-07-05T05:10:46Z"},{"alias_kind":"pith_short_12","alias_value":"BPYWA2IVSKKY","created_at":"2026-07-05T05:10:46Z"},{"alias_kind":"pith_short_16","alias_value":"BPYWA2IVSKKYNYHT","created_at":"2026-07-05T05:10:46Z"},{"alias_kind":"pith_short_8","alias_value":"BPYWA2IV","created_at":"2026-07-05T05:10:46Z"}],"graph_snapshots":[{"event_id":"sha256:36a314444a914ea0940843b453ec074d2b1cf3667e741fd045cd7b2b30bfc810","target":"graph","created_at":"2026-07-05T05:10:46Z","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/2210.14530/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Semantic segmentation is important for scene understanding. To address the scenes of adverse illumination conditions of natural images, thermal infrared (TIR) images are introduced. Most existing RGB-T semantic segmentation methods follow three cross-modal fusion paradigms, i.e. encoder fusion, decoder fusion, and feature fusion. Some methods, unfortunately, ignore the properties of RGB and TIR features or the properties of features at different levels. In this paper, we propose a novel feature fusion-based network for RGB-T semantic segmentation, named \\emph{LASNet}, which follows three steps","authors_text":"Dan Zeng, Gongyang Li, Xinpeng Zhang, Yike Wang, Zhi Liu","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2022-10-26T07:42:34Z","title":"RGB-T Semantic Segmentation with Location, Activation, and Sharpening"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2210.14530","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:db6e1ef259973b9019f85778ba21b48bee35bacb46c52f8a205cccafee04b85e","target":"record","created_at":"2026-07-05T05:10:46Z","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":"f48425b3fa5032eb6172c781fe5545fd704d3b0ed710a660b8c3fcc627db5645","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2022-10-26T07:42:34Z","title_canon_sha256":"b7f3ed42bc64971dd7bff6adcfd5019c9f02f5f2ee25323cb43b7c184d45409d"},"schema_version":"1.0","source":{"id":"2210.14530","kind":"arxiv","version":1}},"canonical_sha256":"0bf1606915929586e0f3cc92f180478daf78a2637b5c9bf8868e057c29c8bea0","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0bf1606915929586e0f3cc92f180478daf78a2637b5c9bf8868e057c29c8bea0","first_computed_at":"2026-07-05T05:10:46.462451Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:10:46.462451Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"cuGDfXQ0EXXHq1A4IJ4CmJNhkqp1cdhPu/qGrkbVKJ7ms/SrhocfjF6VM1k5ECaseZoU99Y2J3IvD+poId5JAw==","signature_status":"signed_v1","signed_at":"2026-07-05T05:10:46.462842Z","signed_message":"canonical_sha256_bytes"},"source_id":"2210.14530","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:db6e1ef259973b9019f85778ba21b48bee35bacb46c52f8a205cccafee04b85e","sha256:36a314444a914ea0940843b453ec074d2b1cf3667e741fd045cd7b2b30bfc810"],"state_sha256":"2b47b03afe22505d22f383599f423ab0712ed3a703281972c5ede7f51bcedb61"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"hPTLRVkWUXc3P4sTmdSXAufFR71VZxbzvBSif229pj/NEPdajdSMM4rpkl2LeGzx7sIOlGgodJQOYTqycNwFCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-07T05:49:19.374789Z","bundle_sha256":"85f1f00a2fd4147b59dde04eeb2ea6fe69d91c266a58f70b5aecb0a927e0d006"}}