{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:JVAOJB2K4RFOHMLKGJ6QJ5PSTF","short_pith_number":"pith:JVAOJB2K","canonical_record":{"source":{"id":"1705.04358","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-05-11T19:25:37Z","cross_cats_sorted":[],"title_canon_sha256":"73245a3c5d9a1f03208e9e9e6cec9ad50bad54bebbb46d8a6720dbbad1ed2bd8","abstract_canon_sha256":"7035caca9ef0228ead7316d10704b2f10ac1d14877f7966dc109fd8a4cde04f8"},"schema_version":"1.0"},"canonical_sha256":"4d40e4874ae44ae3b16a327d04f5f2995ce202a84a27c1fa5d95e3b5ced7b039","source":{"kind":"arxiv","id":"1705.04358","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1705.04358","created_at":"2026-05-18T00:43:12Z"},{"alias_kind":"arxiv_version","alias_value":"1705.04358v2","created_at":"2026-05-18T00:43:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1705.04358","created_at":"2026-05-18T00:43:12Z"},{"alias_kind":"pith_short_12","alias_value":"JVAOJB2K4RFO","created_at":"2026-05-18T12:31:24Z"},{"alias_kind":"pith_short_16","alias_value":"JVAOJB2K4RFOHMLK","created_at":"2026-05-18T12:31:24Z"},{"alias_kind":"pith_short_8","alias_value":"JVAOJB2K","created_at":"2026-05-18T12:31:24Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:JVAOJB2K4RFOHMLKGJ6QJ5PSTF","target":"record","payload":{"canonical_record":{"source":{"id":"1705.04358","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-05-11T19:25:37Z","cross_cats_sorted":[],"title_canon_sha256":"73245a3c5d9a1f03208e9e9e6cec9ad50bad54bebbb46d8a6720dbbad1ed2bd8","abstract_canon_sha256":"7035caca9ef0228ead7316d10704b2f10ac1d14877f7966dc109fd8a4cde04f8"},"schema_version":"1.0"},"canonical_sha256":"4d40e4874ae44ae3b16a327d04f5f2995ce202a84a27c1fa5d95e3b5ced7b039","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:43:12.933494Z","signature_b64":"gDrqPz/PRsinSUz0iFI2mRHv5AUIMlICwPx9SAV4mztm2Do/pPrVO7wx2wRmP8nDHkF1tVxAS1p8FlnRupsIDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4d40e4874ae44ae3b16a327d04f5f2995ce202a84a27c1fa5d95e3b5ced7b039","last_reissued_at":"2026-05-18T00:43:12.932857Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:43:12.932857Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1705.04358","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":"2026-05-18T00:43:12Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"gAjrQTdJYYZVyDYWyObgFEnChRJwVRs7SXtoRySRGym0im0WH0QHfBG5n8aGtA6RAb3b2bWYu73Pyiwhj0L2Ag==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-26T22:19:37.251978Z"},"content_sha256":"a66212c582fc2379722cc83502e96c6eda51e99199da97e81fc5f9ee49575267","schema_version":"1.0","event_id":"sha256:a66212c582fc2379722cc83502e96c6eda51e99199da97e81fc5f9ee49575267"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:JVAOJB2K4RFOHMLKGJ6QJ5PSTF","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Object-Level Context Modeling For Scene Classification with Context-CNN","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Anil Kumar Nelakanti, Syed Ashar Javed","submitted_at":"2017-05-11T19:25:37Z","abstract_excerpt":"Convolutional Neural Networks (CNNs) have been used extensively for computer vision tasks and produce rich feature representation for objects or parts of an image. But reasoning about scenes requires integration between the low-level feature representations and the high-level semantic information. We propose a deep network architecture which models the semantic context of scenes by capturing object-level information. We use Long Short Term Memory(LSTM) units in conjunction with object proposals to incorporate object-object relationship and object-scene relationship in an end-to-end trainable m"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1705.04358","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":"2026-05-18T00:43:12Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"vtG+nujS4sZ/8I+TszIRxBxyd8yGsut50r3tazGRZ9F1sqqWfBjRmPYhaKCwns3PlMRql9Uf3hkvxKxd29hIDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-26T22:19:37.252332Z"},"content_sha256":"d2502337e3aa26732bf60e704874eed3b042e7a5c05fb79f3b4da72fdb54831c","schema_version":"1.0","event_id":"sha256:d2502337e3aa26732bf60e704874eed3b042e7a5c05fb79f3b4da72fdb54831c"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/JVAOJB2K4RFOHMLKGJ6QJ5PSTF/bundle.json","state_url":"https://pith.science/pith/JVAOJB2K4RFOHMLKGJ6QJ5PSTF/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/JVAOJB2K4RFOHMLKGJ6QJ5PSTF/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":"2026-06-26T22:19:37Z","links":{"resolver":"https://pith.science/pith/JVAOJB2K4RFOHMLKGJ6QJ5PSTF","bundle":"https://pith.science/pith/JVAOJB2K4RFOHMLKGJ6QJ5PSTF/bundle.json","state":"https://pith.science/pith/JVAOJB2K4RFOHMLKGJ6QJ5PSTF/state.json","well_known_bundle":"https://pith.science/.well-known/pith/JVAOJB2K4RFOHMLKGJ6QJ5PSTF/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:JVAOJB2K4RFOHMLKGJ6QJ5PSTF","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":"7035caca9ef0228ead7316d10704b2f10ac1d14877f7966dc109fd8a4cde04f8","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-05-11T19:25:37Z","title_canon_sha256":"73245a3c5d9a1f03208e9e9e6cec9ad50bad54bebbb46d8a6720dbbad1ed2bd8"},"schema_version":"1.0","source":{"id":"1705.04358","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1705.04358","created_at":"2026-05-18T00:43:12Z"},{"alias_kind":"arxiv_version","alias_value":"1705.04358v2","created_at":"2026-05-18T00:43:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1705.04358","created_at":"2026-05-18T00:43:12Z"},{"alias_kind":"pith_short_12","alias_value":"JVAOJB2K4RFO","created_at":"2026-05-18T12:31:24Z"},{"alias_kind":"pith_short_16","alias_value":"JVAOJB2K4RFOHMLK","created_at":"2026-05-18T12:31:24Z"},{"alias_kind":"pith_short_8","alias_value":"JVAOJB2K","created_at":"2026-05-18T12:31:24Z"}],"graph_snapshots":[{"event_id":"sha256:d2502337e3aa26732bf60e704874eed3b042e7a5c05fb79f3b4da72fdb54831c","target":"graph","created_at":"2026-05-18T00:43:12Z","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":"Convolutional Neural Networks (CNNs) have been used extensively for computer vision tasks and produce rich feature representation for objects or parts of an image. But reasoning about scenes requires integration between the low-level feature representations and the high-level semantic information. We propose a deep network architecture which models the semantic context of scenes by capturing object-level information. We use Long Short Term Memory(LSTM) units in conjunction with object proposals to incorporate object-object relationship and object-scene relationship in an end-to-end trainable m","authors_text":"Anil Kumar Nelakanti, Syed Ashar Javed","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-05-11T19:25:37Z","title":"Object-Level Context Modeling For Scene Classification with Context-CNN"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1705.04358","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:a66212c582fc2379722cc83502e96c6eda51e99199da97e81fc5f9ee49575267","target":"record","created_at":"2026-05-18T00:43:12Z","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":"7035caca9ef0228ead7316d10704b2f10ac1d14877f7966dc109fd8a4cde04f8","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-05-11T19:25:37Z","title_canon_sha256":"73245a3c5d9a1f03208e9e9e6cec9ad50bad54bebbb46d8a6720dbbad1ed2bd8"},"schema_version":"1.0","source":{"id":"1705.04358","kind":"arxiv","version":2}},"canonical_sha256":"4d40e4874ae44ae3b16a327d04f5f2995ce202a84a27c1fa5d95e3b5ced7b039","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4d40e4874ae44ae3b16a327d04f5f2995ce202a84a27c1fa5d95e3b5ced7b039","first_computed_at":"2026-05-18T00:43:12.932857Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:43:12.932857Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"gDrqPz/PRsinSUz0iFI2mRHv5AUIMlICwPx9SAV4mztm2Do/pPrVO7wx2wRmP8nDHkF1tVxAS1p8FlnRupsIDw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:43:12.933494Z","signed_message":"canonical_sha256_bytes"},"source_id":"1705.04358","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a66212c582fc2379722cc83502e96c6eda51e99199da97e81fc5f9ee49575267","sha256:d2502337e3aa26732bf60e704874eed3b042e7a5c05fb79f3b4da72fdb54831c"],"state_sha256":"8142aa45019fac5b3713b32a87e5b96bcb4df30b9f63a217e9d2827c7557e222"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"i+xGR9sfqPCPZEAzXfUgDwybDRwmgXy8x8fLYWVfRP/3sXzo2nMZjXwWD4JUovMS8qtsAsjbZZzFozjLPytYCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-26T22:19:37.254244Z","bundle_sha256":"b56fbc1ac87204edda43b07e138aba53d2a8e70902c46f6c5ee1d60f112367c5"}}