{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2015:4LJQYEFBLMSUXW7NUKG32L5EYC","short_pith_number":"pith:4LJQYEFB","canonical_record":{"source":{"id":"1504.07889","kind":"arxiv","version":6},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-04-29T15:23:58Z","cross_cats_sorted":[],"title_canon_sha256":"086264fef958f0b5d99900dbb39db107ce27f4f6f65fdd4e91fc03bf027dce4e","abstract_canon_sha256":"4cfb0329c7912f941ae97ef1fe03641d6e5d899a5f774d25da34f790b83a6caa"},"schema_version":"1.0"},"canonical_sha256":"e2d30c10a15b254bdbeda28dbd2fa4c0bd487917bc350f10aed8a9ec64d3b08b","source":{"kind":"arxiv","id":"1504.07889","version":6},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1504.07889","created_at":"2026-05-18T00:43:18Z"},{"alias_kind":"arxiv_version","alias_value":"1504.07889v6","created_at":"2026-05-18T00:43:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1504.07889","created_at":"2026-05-18T00:43:18Z"},{"alias_kind":"pith_short_12","alias_value":"4LJQYEFBLMSU","created_at":"2026-05-18T12:29:05Z"},{"alias_kind":"pith_short_16","alias_value":"4LJQYEFBLMSUXW7N","created_at":"2026-05-18T12:29:05Z"},{"alias_kind":"pith_short_8","alias_value":"4LJQYEFB","created_at":"2026-05-18T12:29:05Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2015:4LJQYEFBLMSUXW7NUKG32L5EYC","target":"record","payload":{"canonical_record":{"source":{"id":"1504.07889","kind":"arxiv","version":6},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-04-29T15:23:58Z","cross_cats_sorted":[],"title_canon_sha256":"086264fef958f0b5d99900dbb39db107ce27f4f6f65fdd4e91fc03bf027dce4e","abstract_canon_sha256":"4cfb0329c7912f941ae97ef1fe03641d6e5d899a5f774d25da34f790b83a6caa"},"schema_version":"1.0"},"canonical_sha256":"e2d30c10a15b254bdbeda28dbd2fa4c0bd487917bc350f10aed8a9ec64d3b08b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:43:18.860340Z","signature_b64":"tYqlqZCj7Xz+oZnf8ofZWLioNXJctYTvmPfyrINtTADiFs+XcpOQv/jI8doWdsyGUNzqO9S2JL45iZ/iMaAgCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e2d30c10a15b254bdbeda28dbd2fa4c0bd487917bc350f10aed8a9ec64d3b08b","last_reissued_at":"2026-05-18T00:43:18.859748Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:43:18.859748Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1504.07889","source_version":6,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:43:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"hUyhbQNBlHEBGENmxaJZPP3DvstX3WI8WgHjtdIXSqaPtzts2XTtTM/lnakqYWtBePgr2Xw+8Nr2lwkZME1kAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T07:34:16.560237Z"},"content_sha256":"ddcac8d30560079f2085ffdb87c89d7335c47417e57430f73875b915e2a74160","schema_version":"1.0","event_id":"sha256:ddcac8d30560079f2085ffdb87c89d7335c47417e57430f73875b915e2a74160"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2015:4LJQYEFBLMSUXW7NUKG32L5EYC","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Bilinear CNNs for Fine-grained Visual Recognition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Aruni RoyChowdhury, Subhransu Maji, Tsung-Yu Lin","submitted_at":"2015-04-29T15:23:58Z","abstract_excerpt":"We present a simple and effective architecture for fine-grained visual recognition called Bilinear Convolutional Neural Networks (B-CNNs). These networks represent an image as a pooled outer product of features derived from two CNNs and capture localized feature interactions in a translationally invariant manner. B-CNNs belong to the class of orderless texture representations but unlike prior work they can be trained in an end-to-end manner. Our most accurate model obtains 84.1%, 79.4%, 86.9% and 91.3% per-image accuracy on the Caltech-UCSD birds [67], NABirds [64], FGVC aircraft [42], and Sta"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1504.07889","kind":"arxiv","version":6},"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":"2026-05-18T00:43:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"e5SpdVvXr6I0ua/LZs4QFrBXYJxSTgJV1+Wov/iF+OUmDfKalxs2Rlj+UF7UCAEtKBDKQcvjMgzGKufEQQeABQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T07:34:16.560934Z"},"content_sha256":"56aa41205f59886f153a5a39e8db473f3d4cafc2734563b5d6ca4a4f34f44082","schema_version":"1.0","event_id":"sha256:56aa41205f59886f153a5a39e8db473f3d4cafc2734563b5d6ca4a4f34f44082"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/4LJQYEFBLMSUXW7NUKG32L5EYC/bundle.json","state_url":"https://pith.science/pith/4LJQYEFBLMSUXW7NUKG32L5EYC/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/4LJQYEFBLMSUXW7NUKG32L5EYC/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":"2026-05-25T07:34:16Z","links":{"resolver":"https://pith.science/pith/4LJQYEFBLMSUXW7NUKG32L5EYC","bundle":"https://pith.science/pith/4LJQYEFBLMSUXW7NUKG32L5EYC/bundle.json","state":"https://pith.science/pith/4LJQYEFBLMSUXW7NUKG32L5EYC/state.json","well_known_bundle":"https://pith.science/.well-known/pith/4LJQYEFBLMSUXW7NUKG32L5EYC/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2015:4LJQYEFBLMSUXW7NUKG32L5EYC","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":"4cfb0329c7912f941ae97ef1fe03641d6e5d899a5f774d25da34f790b83a6caa","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-04-29T15:23:58Z","title_canon_sha256":"086264fef958f0b5d99900dbb39db107ce27f4f6f65fdd4e91fc03bf027dce4e"},"schema_version":"1.0","source":{"id":"1504.07889","kind":"arxiv","version":6}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1504.07889","created_at":"2026-05-18T00:43:18Z"},{"alias_kind":"arxiv_version","alias_value":"1504.07889v6","created_at":"2026-05-18T00:43:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1504.07889","created_at":"2026-05-18T00:43:18Z"},{"alias_kind":"pith_short_12","alias_value":"4LJQYEFBLMSU","created_at":"2026-05-18T12:29:05Z"},{"alias_kind":"pith_short_16","alias_value":"4LJQYEFBLMSUXW7N","created_at":"2026-05-18T12:29:05Z"},{"alias_kind":"pith_short_8","alias_value":"4LJQYEFB","created_at":"2026-05-18T12:29:05Z"}],"graph_snapshots":[{"event_id":"sha256:56aa41205f59886f153a5a39e8db473f3d4cafc2734563b5d6ca4a4f34f44082","target":"graph","created_at":"2026-05-18T00:43:18Z","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":"We present a simple and effective architecture for fine-grained visual recognition called Bilinear Convolutional Neural Networks (B-CNNs). These networks represent an image as a pooled outer product of features derived from two CNNs and capture localized feature interactions in a translationally invariant manner. B-CNNs belong to the class of orderless texture representations but unlike prior work they can be trained in an end-to-end manner. Our most accurate model obtains 84.1%, 79.4%, 86.9% and 91.3% per-image accuracy on the Caltech-UCSD birds [67], NABirds [64], FGVC aircraft [42], and Sta","authors_text":"Aruni RoyChowdhury, Subhransu Maji, Tsung-Yu Lin","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-04-29T15:23:58Z","title":"Bilinear CNNs for Fine-grained Visual Recognition"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1504.07889","kind":"arxiv","version":6},"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:ddcac8d30560079f2085ffdb87c89d7335c47417e57430f73875b915e2a74160","target":"record","created_at":"2026-05-18T00:43:18Z","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":"4cfb0329c7912f941ae97ef1fe03641d6e5d899a5f774d25da34f790b83a6caa","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-04-29T15:23:58Z","title_canon_sha256":"086264fef958f0b5d99900dbb39db107ce27f4f6f65fdd4e91fc03bf027dce4e"},"schema_version":"1.0","source":{"id":"1504.07889","kind":"arxiv","version":6}},"canonical_sha256":"e2d30c10a15b254bdbeda28dbd2fa4c0bd487917bc350f10aed8a9ec64d3b08b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e2d30c10a15b254bdbeda28dbd2fa4c0bd487917bc350f10aed8a9ec64d3b08b","first_computed_at":"2026-05-18T00:43:18.859748Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:43:18.859748Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"tYqlqZCj7Xz+oZnf8ofZWLioNXJctYTvmPfyrINtTADiFs+XcpOQv/jI8doWdsyGUNzqO9S2JL45iZ/iMaAgCg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:43:18.860340Z","signed_message":"canonical_sha256_bytes"},"source_id":"1504.07889","source_kind":"arxiv","source_version":6}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ddcac8d30560079f2085ffdb87c89d7335c47417e57430f73875b915e2a74160","sha256:56aa41205f59886f153a5a39e8db473f3d4cafc2734563b5d6ca4a4f34f44082"],"state_sha256":"515aa7dedf7aee99d66b4f9ef946eadc0ada5aac073344ad1b1cd5ee08e42ad7"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"iL/9eevwhqgGMew4UZP+xe+4qy9WD2VmtnfYT+eol9TLMsj/r4ejD6PLLGsNblFMYM6995KGiOy/KH5PT9W8Bw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-25T07:34:16.564596Z","bundle_sha256":"a949ec09bd33d964c8726628839840b037c39db507511ef0b634d563adcf3924"}}