{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:NTTKL5OY5QI6XTFQ3ZUTFSS2BZ","short_pith_number":"pith:NTTKL5OY","canonical_record":{"source":{"id":"1611.05435","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-11-16T20:46:38Z","cross_cats_sorted":[],"title_canon_sha256":"07b9d6185f307e8b981426e8a213d68a8d3517cfec4fdd545395a1d71ae2e13d","abstract_canon_sha256":"c77e4b3779b68b70302990b8be000f7eab01ecf56b7fadac92ec9f49b79c6a64"},"schema_version":"1.0"},"canonical_sha256":"6ce6a5f5d8ec11ebccb0de6932ca5a0e4077e48dbe802350fea628f9f9e48c4d","source":{"kind":"arxiv","id":"1611.05435","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1611.05435","created_at":"2026-05-18T00:57:25Z"},{"alias_kind":"arxiv_version","alias_value":"1611.05435v2","created_at":"2026-05-18T00:57:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1611.05435","created_at":"2026-05-18T00:57:25Z"},{"alias_kind":"pith_short_12","alias_value":"NTTKL5OY5QI6","created_at":"2026-05-18T12:30:36Z"},{"alias_kind":"pith_short_16","alias_value":"NTTKL5OY5QI6XTFQ","created_at":"2026-05-18T12:30:36Z"},{"alias_kind":"pith_short_8","alias_value":"NTTKL5OY","created_at":"2026-05-18T12:30:36Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:NTTKL5OY5QI6XTFQ3ZUTFSS2BZ","target":"record","payload":{"canonical_record":{"source":{"id":"1611.05435","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-11-16T20:46:38Z","cross_cats_sorted":[],"title_canon_sha256":"07b9d6185f307e8b981426e8a213d68a8d3517cfec4fdd545395a1d71ae2e13d","abstract_canon_sha256":"c77e4b3779b68b70302990b8be000f7eab01ecf56b7fadac92ec9f49b79c6a64"},"schema_version":"1.0"},"canonical_sha256":"6ce6a5f5d8ec11ebccb0de6932ca5a0e4077e48dbe802350fea628f9f9e48c4d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:57:25.164330Z","signature_b64":"+QBcXc5o8H9AAgqkmzFaOEClNn0kJ4qkvzjfUjY5MQ+/0VXgK9lgQRWe04yK+eGqoX5EMa4fJYpdD2Mzuu0SDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6ce6a5f5d8ec11ebccb0de6932ca5a0e4077e48dbe802350fea628f9f9e48c4d","last_reissued_at":"2026-05-18T00:57:25.163777Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:57:25.163777Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1611.05435","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:57:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"NKqPy5AA4LJQ7712s/ajCCgYu+TVkAAV1NNvFE4wsJCSmlIYL82Yqzo3z9ljA4zTUHIFpIoeUeoiT1WV0XkzDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T10:35:09.822646Z"},"content_sha256":"bc98a37efa0d4e345817d885f11614e40cccd0047142f6b457e8cd6fd360a2af","schema_version":"1.0","event_id":"sha256:bc98a37efa0d4e345817d885f11614e40cccd0047142f6b457e8cd6fd360a2af"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:NTTKL5OY5QI6XTFQ3ZUTFSS2BZ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Convolutional Gated Recurrent Networks for Video Segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Martin Jagersand, Mennatullah Siam, Nilanjan Ray, Sepehr Valipour","submitted_at":"2016-11-16T20:46:38Z","abstract_excerpt":"Semantic segmentation has recently witnessed major progress, where fully convolutional neural networks have shown to perform well. However, most of the previous work focused on improving single image segmentation. To our knowledge, no prior work has made use of temporal video information in a recurrent network. In this paper, we introduce a novel approach to implicitly utilize temporal data in videos for online semantic segmentation. The method relies on a fully convolutional network that is embedded into a gated recurrent architecture. This design receives a sequence of consecutive video fram"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1611.05435","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"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":"2026-05-18T00:57:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ii3+hXZmCaB5SakT6XVqXi7Dgnep5UzGEAzkxVGtzV8l98wq7mABhLnOxgNnzPGm7nWhmySnYbCA38qUhfSCBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T10:35:09.823024Z"},"content_sha256":"29ade9dd3977a9566de720ae4ab3f0d0a423a2b8cd944eb1f3a41673415475d5","schema_version":"1.0","event_id":"sha256:29ade9dd3977a9566de720ae4ab3f0d0a423a2b8cd944eb1f3a41673415475d5"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/NTTKL5OY5QI6XTFQ3ZUTFSS2BZ/bundle.json","state_url":"https://pith.science/pith/NTTKL5OY5QI6XTFQ3ZUTFSS2BZ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/NTTKL5OY5QI6XTFQ3ZUTFSS2BZ/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":"2026-05-26T10:35:09Z","links":{"resolver":"https://pith.science/pith/NTTKL5OY5QI6XTFQ3ZUTFSS2BZ","bundle":"https://pith.science/pith/NTTKL5OY5QI6XTFQ3ZUTFSS2BZ/bundle.json","state":"https://pith.science/pith/NTTKL5OY5QI6XTFQ3ZUTFSS2BZ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/NTTKL5OY5QI6XTFQ3ZUTFSS2BZ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:NTTKL5OY5QI6XTFQ3ZUTFSS2BZ","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":"c77e4b3779b68b70302990b8be000f7eab01ecf56b7fadac92ec9f49b79c6a64","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-11-16T20:46:38Z","title_canon_sha256":"07b9d6185f307e8b981426e8a213d68a8d3517cfec4fdd545395a1d71ae2e13d"},"schema_version":"1.0","source":{"id":"1611.05435","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1611.05435","created_at":"2026-05-18T00:57:25Z"},{"alias_kind":"arxiv_version","alias_value":"1611.05435v2","created_at":"2026-05-18T00:57:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1611.05435","created_at":"2026-05-18T00:57:25Z"},{"alias_kind":"pith_short_12","alias_value":"NTTKL5OY5QI6","created_at":"2026-05-18T12:30:36Z"},{"alias_kind":"pith_short_16","alias_value":"NTTKL5OY5QI6XTFQ","created_at":"2026-05-18T12:30:36Z"},{"alias_kind":"pith_short_8","alias_value":"NTTKL5OY","created_at":"2026-05-18T12:30:36Z"}],"graph_snapshots":[{"event_id":"sha256:29ade9dd3977a9566de720ae4ab3f0d0a423a2b8cd944eb1f3a41673415475d5","target":"graph","created_at":"2026-05-18T00:57:25Z","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"},"paper":{"abstract_excerpt":"Semantic segmentation has recently witnessed major progress, where fully convolutional neural networks have shown to perform well. However, most of the previous work focused on improving single image segmentation. To our knowledge, no prior work has made use of temporal video information in a recurrent network. In this paper, we introduce a novel approach to implicitly utilize temporal data in videos for online semantic segmentation. The method relies on a fully convolutional network that is embedded into a gated recurrent architecture. This design receives a sequence of consecutive video fram","authors_text":"Martin Jagersand, Mennatullah Siam, Nilanjan Ray, Sepehr Valipour","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-11-16T20:46:38Z","title":"Convolutional Gated Recurrent Networks for Video Segmentation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1611.05435","kind":"arxiv","version":2},"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:bc98a37efa0d4e345817d885f11614e40cccd0047142f6b457e8cd6fd360a2af","target":"record","created_at":"2026-05-18T00:57:25Z","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":"c77e4b3779b68b70302990b8be000f7eab01ecf56b7fadac92ec9f49b79c6a64","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-11-16T20:46:38Z","title_canon_sha256":"07b9d6185f307e8b981426e8a213d68a8d3517cfec4fdd545395a1d71ae2e13d"},"schema_version":"1.0","source":{"id":"1611.05435","kind":"arxiv","version":2}},"canonical_sha256":"6ce6a5f5d8ec11ebccb0de6932ca5a0e4077e48dbe802350fea628f9f9e48c4d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"6ce6a5f5d8ec11ebccb0de6932ca5a0e4077e48dbe802350fea628f9f9e48c4d","first_computed_at":"2026-05-18T00:57:25.163777Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:57:25.163777Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"+QBcXc5o8H9AAgqkmzFaOEClNn0kJ4qkvzjfUjY5MQ+/0VXgK9lgQRWe04yK+eGqoX5EMa4fJYpdD2Mzuu0SDQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:57:25.164330Z","signed_message":"canonical_sha256_bytes"},"source_id":"1611.05435","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:bc98a37efa0d4e345817d885f11614e40cccd0047142f6b457e8cd6fd360a2af","sha256:29ade9dd3977a9566de720ae4ab3f0d0a423a2b8cd944eb1f3a41673415475d5"],"state_sha256":"610e75106b84daec24ff053388c6cc5082be6f202e853cbacca9f2d00cf78a22"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"f3vPVMkTf/WuBRBAwRmkMmdCzibFNv8Ffeh6BpUQ1WpQkpTU+43fXTf9KzrcONIAugxqyyVZ2LxuICwvjlttBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-26T10:35:09.825090Z","bundle_sha256":"2d08e0377493abb13ae6f5e90f48ecd2a25b1262f93aa2abd49994053d660de2"}}