{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:Y2CDZR3UX6UW36KAUC4TD56B7I","short_pith_number":"pith:Y2CDZR3U","canonical_record":{"source":{"id":"1809.00461","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-09-03T06:16:13Z","cross_cats_sorted":[],"title_canon_sha256":"b3759913039b5b4989cdf1af7f57107fb8e13bdbf359c0d6a83acc50b2eb47b6","abstract_canon_sha256":"b937818cf7f3b6a8273f0068895161cbce387f994ecd22a14c73793f5ad59564"},"schema_version":"1.0"},"canonical_sha256":"c6843cc774bfa96df940a0b931f7c1fa17f83b94caa33a8705f84b4fa2a8c328","source":{"kind":"arxiv","id":"1809.00461","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1809.00461","created_at":"2026-05-18T00:06:35Z"},{"alias_kind":"arxiv_version","alias_value":"1809.00461v1","created_at":"2026-05-18T00:06:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1809.00461","created_at":"2026-05-18T00:06:35Z"},{"alias_kind":"pith_short_12","alias_value":"Y2CDZR3UX6UW","created_at":"2026-05-18T12:33:04Z"},{"alias_kind":"pith_short_16","alias_value":"Y2CDZR3UX6UW36KA","created_at":"2026-05-18T12:33:04Z"},{"alias_kind":"pith_short_8","alias_value":"Y2CDZR3U","created_at":"2026-05-18T12:33:04Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:Y2CDZR3UX6UW36KAUC4TD56B7I","target":"record","payload":{"canonical_record":{"source":{"id":"1809.00461","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-09-03T06:16:13Z","cross_cats_sorted":[],"title_canon_sha256":"b3759913039b5b4989cdf1af7f57107fb8e13bdbf359c0d6a83acc50b2eb47b6","abstract_canon_sha256":"b937818cf7f3b6a8273f0068895161cbce387f994ecd22a14c73793f5ad59564"},"schema_version":"1.0"},"canonical_sha256":"c6843cc774bfa96df940a0b931f7c1fa17f83b94caa33a8705f84b4fa2a8c328","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:06:35.172755Z","signature_b64":"9lCiVehM/1Gg6fAPr+X4c+h70MszCQ4Ors+I3kKwu7i1lPQENh2cFsaR9PLlR9WtYdjN9cV6cgihQckJcH7hAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c6843cc774bfa96df940a0b931f7c1fa17f83b94caa33a8705f84b4fa2a8c328","last_reissued_at":"2026-05-18T00:06:35.172294Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:06:35.172294Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1809.00461","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":"2026-05-18T00:06:35Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0qy972DhZpwzAy4xWZ08zihLT47B9x3Q324Dx+BqHv2RAPaG+Yi8F7hRnLV5eMy3V4KIEJEMg6ebUyW0gp6HAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-24T17:20:20.860883Z"},"content_sha256":"14ac4e74fe663bb8bca4e6f5058d03401e583f6d64c16dc765cb32a2ae520fb7","schema_version":"1.0","event_id":"sha256:14ac4e74fe663bb8bca4e6f5058d03401e583f6d64c16dc765cb32a2ae520fb7"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:Y2CDZR3UX6UW36KAUC4TD56B7I","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"YouTube-VOS: Sequence-to-Sequence Video Object Segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Brian Price, Dingcheng Yue, Jianchao Yang, Linjie Yang, Ning Xu, Scott Cohen, Thomas Huang, Yuchen Fan, Yuchen Liang","submitted_at":"2018-09-03T06:16:13Z","abstract_excerpt":"Learning long-term spatial-temporal features are critical for many video analysis tasks. However, existing video segmentation methods predominantly rely on static image segmentation techniques, and methods capturing temporal dependency for segmentation have to depend on pretrained optical flow models, leading to suboptimal solutions for the problem. End-to-end sequential learning to explore spatial-temporal features for video segmentation is largely limited by the scale of available video segmentation datasets, i.e., even the largest video segmentation dataset only contains 90 short video clip"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1809.00461","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":""},"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":"2026-05-18T00:06:35Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"kuvW43lsUVaGY7tMnkyWwCQYbLXqo7U/g/g594XyVSrbD1AkVuT6KUNBC0Os7jRTxQcrAgA65y+1+KAna3/TBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-24T17:20:20.861242Z"},"content_sha256":"17be1f047a25ef6801b0841e0ccc70eed8f611b9af92ba5ad9a782e7099ae077","schema_version":"1.0","event_id":"sha256:17be1f047a25ef6801b0841e0ccc70eed8f611b9af92ba5ad9a782e7099ae077"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/Y2CDZR3UX6UW36KAUC4TD56B7I/bundle.json","state_url":"https://pith.science/pith/Y2CDZR3UX6UW36KAUC4TD56B7I/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/Y2CDZR3UX6UW36KAUC4TD56B7I/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":"2026-05-24T17:20:20Z","links":{"resolver":"https://pith.science/pith/Y2CDZR3UX6UW36KAUC4TD56B7I","bundle":"https://pith.science/pith/Y2CDZR3UX6UW36KAUC4TD56B7I/bundle.json","state":"https://pith.science/pith/Y2CDZR3UX6UW36KAUC4TD56B7I/state.json","well_known_bundle":"https://pith.science/.well-known/pith/Y2CDZR3UX6UW36KAUC4TD56B7I/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:Y2CDZR3UX6UW36KAUC4TD56B7I","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":"b937818cf7f3b6a8273f0068895161cbce387f994ecd22a14c73793f5ad59564","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-09-03T06:16:13Z","title_canon_sha256":"b3759913039b5b4989cdf1af7f57107fb8e13bdbf359c0d6a83acc50b2eb47b6"},"schema_version":"1.0","source":{"id":"1809.00461","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1809.00461","created_at":"2026-05-18T00:06:35Z"},{"alias_kind":"arxiv_version","alias_value":"1809.00461v1","created_at":"2026-05-18T00:06:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1809.00461","created_at":"2026-05-18T00:06:35Z"},{"alias_kind":"pith_short_12","alias_value":"Y2CDZR3UX6UW","created_at":"2026-05-18T12:33:04Z"},{"alias_kind":"pith_short_16","alias_value":"Y2CDZR3UX6UW36KA","created_at":"2026-05-18T12:33:04Z"},{"alias_kind":"pith_short_8","alias_value":"Y2CDZR3U","created_at":"2026-05-18T12:33:04Z"}],"graph_snapshots":[{"event_id":"sha256:17be1f047a25ef6801b0841e0ccc70eed8f611b9af92ba5ad9a782e7099ae077","target":"graph","created_at":"2026-05-18T00:06:35Z","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":"Learning long-term spatial-temporal features are critical for many video analysis tasks. However, existing video segmentation methods predominantly rely on static image segmentation techniques, and methods capturing temporal dependency for segmentation have to depend on pretrained optical flow models, leading to suboptimal solutions for the problem. End-to-end sequential learning to explore spatial-temporal features for video segmentation is largely limited by the scale of available video segmentation datasets, i.e., even the largest video segmentation dataset only contains 90 short video clip","authors_text":"Brian Price, Dingcheng Yue, Jianchao Yang, Linjie Yang, Ning Xu, Scott Cohen, Thomas Huang, Yuchen Fan, Yuchen Liang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-09-03T06:16:13Z","title":"YouTube-VOS: Sequence-to-Sequence Video Object Segmentation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1809.00461","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":"sha256:14ac4e74fe663bb8bca4e6f5058d03401e583f6d64c16dc765cb32a2ae520fb7","target":"record","created_at":"2026-05-18T00:06:35Z","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":"b937818cf7f3b6a8273f0068895161cbce387f994ecd22a14c73793f5ad59564","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-09-03T06:16:13Z","title_canon_sha256":"b3759913039b5b4989cdf1af7f57107fb8e13bdbf359c0d6a83acc50b2eb47b6"},"schema_version":"1.0","source":{"id":"1809.00461","kind":"arxiv","version":1}},"canonical_sha256":"c6843cc774bfa96df940a0b931f7c1fa17f83b94caa33a8705f84b4fa2a8c328","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c6843cc774bfa96df940a0b931f7c1fa17f83b94caa33a8705f84b4fa2a8c328","first_computed_at":"2026-05-18T00:06:35.172294Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:06:35.172294Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"9lCiVehM/1Gg6fAPr+X4c+h70MszCQ4Ors+I3kKwu7i1lPQENh2cFsaR9PLlR9WtYdjN9cV6cgihQckJcH7hAw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:06:35.172755Z","signed_message":"canonical_sha256_bytes"},"source_id":"1809.00461","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:14ac4e74fe663bb8bca4e6f5058d03401e583f6d64c16dc765cb32a2ae520fb7","sha256:17be1f047a25ef6801b0841e0ccc70eed8f611b9af92ba5ad9a782e7099ae077"],"state_sha256":"687fbccbfdfda117aae81cb3ac44797fa1a53b8304df69202b1d78f6db7cbf0e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"yF6oUh9s6aiMh2/3wH934lkuHKaE7xVap40zFPge+5BSHsRTaF+hw8nIJCWR4VBg1W0uVo8J1QLq8cE9EwovCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-24T17:20:20.863515Z","bundle_sha256":"f28581327cf2633ec467750d9a7755dc538c275c88004bf16238982a693b640d"}}