{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:ZCCOMCQVLEQENIA7Z6XS4WFPRQ","short_pith_number":"pith:ZCCOMCQV","canonical_record":{"source":{"id":"1805.06875","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-05-17T17:36:42Z","cross_cats_sorted":[],"title_canon_sha256":"63a0262f625c2f2e9ff0439f406c81a8203990330a2ab16d53ccb172684961de","abstract_canon_sha256":"084a14364a70d00b219cd6dca0e0dd5c6eb8d7d6b7f191cb4280831c23ce74ee"},"schema_version":"1.0"},"canonical_sha256":"c884e60a15592046a01fcfaf2e58af8c09ddcfc2f137c343c563a270d7636af0","source":{"kind":"arxiv","id":"1805.06875","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1805.06875","created_at":"2026-05-18T00:15:43Z"},{"alias_kind":"arxiv_version","alias_value":"1805.06875v1","created_at":"2026-05-18T00:15:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.06875","created_at":"2026-05-18T00:15:43Z"},{"alias_kind":"pith_short_12","alias_value":"ZCCOMCQVLEQE","created_at":"2026-05-18T12:33:04Z"},{"alias_kind":"pith_short_16","alias_value":"ZCCOMCQVLEQENIA7","created_at":"2026-05-18T12:33:04Z"},{"alias_kind":"pith_short_8","alias_value":"ZCCOMCQV","created_at":"2026-05-18T12:33:04Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:ZCCOMCQVLEQENIA7Z6XS4WFPRQ","target":"record","payload":{"canonical_record":{"source":{"id":"1805.06875","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-05-17T17:36:42Z","cross_cats_sorted":[],"title_canon_sha256":"63a0262f625c2f2e9ff0439f406c81a8203990330a2ab16d53ccb172684961de","abstract_canon_sha256":"084a14364a70d00b219cd6dca0e0dd5c6eb8d7d6b7f191cb4280831c23ce74ee"},"schema_version":"1.0"},"canonical_sha256":"c884e60a15592046a01fcfaf2e58af8c09ddcfc2f137c343c563a270d7636af0","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:15:43.055383Z","signature_b64":"AzhOW2tyh/yLm2hXLMDCQot1h1upUlgjTJOTHlw1gQ2JzBL6Ttqd/z96yS3Q5VGaeHYK4buy4bgvWvHMwo0HBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c884e60a15592046a01fcfaf2e58af8c09ddcfc2f137c343c563a270d7636af0","last_reissued_at":"2026-05-18T00:15:43.054818Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:15:43.054818Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1805.06875","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":"2026-05-18T00:15:43Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"GdWN32oB8wm+fY2hnDuM73537ujtoA61lnWuaqgYiosNdFuMxzzG7LtUCg+z8nIFIfusrGSJT63O0BtbFORcDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T07:30:34.331185Z"},"content_sha256":"5c69eee2739ceacc24984459566feba7c7ce02d72b23f010024043cc653d577a","schema_version":"1.0","event_id":"sha256:5c69eee2739ceacc24984459566feba7c7ce02d72b23f010024043cc653d577a"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:ZCCOMCQVLEQENIA7Z6XS4WFPRQ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"NeuralNetwork-Viterbi: A Framework for Weakly Supervised Video Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Ahsan Iqbal, Alexander Richard, Hilde Kuehne, Juergen Gall","submitted_at":"2018-05-17T17:36:42Z","abstract_excerpt":"Video learning is an important task in computer vision and has experienced increasing interest over the recent years. Since even a small amount of videos easily comprises several million frames, methods that do not rely on a frame-level annotation are of special importance. In this work, we propose a novel learning algorithm with a Viterbi-based loss that allows for online and incremental learning of weakly annotated video data. We moreover show that explicit context and length modeling leads to huge improvements in video segmentation and labeling tasks andinclude these models into our framewo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.06875","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":"2026-05-18T00:15:43Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Lb753jEQi+RLh2L1DQ4kN7oniQpH+ilXMkdaFTVCgeTCiemfVYSgbH5gZadbWSPCpejhMaQYZAoikp3YpWC2Aw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T07:30:34.331944Z"},"content_sha256":"d987dc84feabe997e2223c961b19ca4dfb974cba01c1510e1265d2fa318492a1","schema_version":"1.0","event_id":"sha256:d987dc84feabe997e2223c961b19ca4dfb974cba01c1510e1265d2fa318492a1"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ZCCOMCQVLEQENIA7Z6XS4WFPRQ/bundle.json","state_url":"https://pith.science/pith/ZCCOMCQVLEQENIA7Z6XS4WFPRQ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ZCCOMCQVLEQENIA7Z6XS4WFPRQ/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":"2026-05-30T07:30:34Z","links":{"resolver":"https://pith.science/pith/ZCCOMCQVLEQENIA7Z6XS4WFPRQ","bundle":"https://pith.science/pith/ZCCOMCQVLEQENIA7Z6XS4WFPRQ/bundle.json","state":"https://pith.science/pith/ZCCOMCQVLEQENIA7Z6XS4WFPRQ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ZCCOMCQVLEQENIA7Z6XS4WFPRQ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:ZCCOMCQVLEQENIA7Z6XS4WFPRQ","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":"084a14364a70d00b219cd6dca0e0dd5c6eb8d7d6b7f191cb4280831c23ce74ee","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-05-17T17:36:42Z","title_canon_sha256":"63a0262f625c2f2e9ff0439f406c81a8203990330a2ab16d53ccb172684961de"},"schema_version":"1.0","source":{"id":"1805.06875","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1805.06875","created_at":"2026-05-18T00:15:43Z"},{"alias_kind":"arxiv_version","alias_value":"1805.06875v1","created_at":"2026-05-18T00:15:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.06875","created_at":"2026-05-18T00:15:43Z"},{"alias_kind":"pith_short_12","alias_value":"ZCCOMCQVLEQE","created_at":"2026-05-18T12:33:04Z"},{"alias_kind":"pith_short_16","alias_value":"ZCCOMCQVLEQENIA7","created_at":"2026-05-18T12:33:04Z"},{"alias_kind":"pith_short_8","alias_value":"ZCCOMCQV","created_at":"2026-05-18T12:33:04Z"}],"graph_snapshots":[{"event_id":"sha256:d987dc84feabe997e2223c961b19ca4dfb974cba01c1510e1265d2fa318492a1","target":"graph","created_at":"2026-05-18T00:15:43Z","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":"Video learning is an important task in computer vision and has experienced increasing interest over the recent years. Since even a small amount of videos easily comprises several million frames, methods that do not rely on a frame-level annotation are of special importance. In this work, we propose a novel learning algorithm with a Viterbi-based loss that allows for online and incremental learning of weakly annotated video data. We moreover show that explicit context and length modeling leads to huge improvements in video segmentation and labeling tasks andinclude these models into our framewo","authors_text":"Ahsan Iqbal, Alexander Richard, Hilde Kuehne, Juergen Gall","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-05-17T17:36:42Z","title":"NeuralNetwork-Viterbi: A Framework for Weakly Supervised Video Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.06875","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":"sha256:5c69eee2739ceacc24984459566feba7c7ce02d72b23f010024043cc653d577a","target":"record","created_at":"2026-05-18T00:15:43Z","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":"084a14364a70d00b219cd6dca0e0dd5c6eb8d7d6b7f191cb4280831c23ce74ee","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-05-17T17:36:42Z","title_canon_sha256":"63a0262f625c2f2e9ff0439f406c81a8203990330a2ab16d53ccb172684961de"},"schema_version":"1.0","source":{"id":"1805.06875","kind":"arxiv","version":1}},"canonical_sha256":"c884e60a15592046a01fcfaf2e58af8c09ddcfc2f137c343c563a270d7636af0","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c884e60a15592046a01fcfaf2e58af8c09ddcfc2f137c343c563a270d7636af0","first_computed_at":"2026-05-18T00:15:43.054818Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:15:43.054818Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"AzhOW2tyh/yLm2hXLMDCQot1h1upUlgjTJOTHlw1gQ2JzBL6Ttqd/z96yS3Q5VGaeHYK4buy4bgvWvHMwo0HBQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:15:43.055383Z","signed_message":"canonical_sha256_bytes"},"source_id":"1805.06875","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:5c69eee2739ceacc24984459566feba7c7ce02d72b23f010024043cc653d577a","sha256:d987dc84feabe997e2223c961b19ca4dfb974cba01c1510e1265d2fa318492a1"],"state_sha256":"041d9e25e4b31865d8cc27cd88b34371720d99c1b5c5ee3fea2cc4c6c0ff1be8"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"D4qsO0MwKULHeFcjX81JRurW3ZRaizIOebFEw/HBnHNuE1W3tD8Xv/R82ti7vb5pabgCtO/Ipgkh17p1VJ5RDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-30T07:30:34.335520Z","bundle_sha256":"3530915c64f7a3345b6d950c0b2e9c73fd69bee5da880a1339562fd6cd0f4788"}}