{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:EWUFKAXMN2DB2IRBLI7GWB46YY","short_pith_number":"pith:EWUFKAXM","canonical_record":{"source":{"id":"2001.01211","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-01-05T11:12:08Z","cross_cats_sorted":[],"title_canon_sha256":"36e54ebf007e24cd2ee6c08d99e551610d6a6e0d76c2adb3a2a671ca0712697d","abstract_canon_sha256":"b2067a9977c57d53810644dd7f486a06adf3306361b8f664b0a392568ff3e85f"},"schema_version":"1.0"},"canonical_sha256":"25a85502ec6e861d22215a3e6b079ec61034f3734dd4c61b66aad5767e9f9d6e","source":{"kind":"arxiv","id":"2001.01211","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2001.01211","created_at":"2026-07-05T00:29:44Z"},{"alias_kind":"arxiv_version","alias_value":"2001.01211v1","created_at":"2026-07-05T00:29:44Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2001.01211","created_at":"2026-07-05T00:29:44Z"},{"alias_kind":"pith_short_12","alias_value":"EWUFKAXMN2DB","created_at":"2026-07-05T00:29:44Z"},{"alias_kind":"pith_short_16","alias_value":"EWUFKAXMN2DB2IRB","created_at":"2026-07-05T00:29:44Z"},{"alias_kind":"pith_short_8","alias_value":"EWUFKAXM","created_at":"2026-07-05T00:29:44Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:EWUFKAXMN2DB2IRBLI7GWB46YY","target":"record","payload":{"canonical_record":{"source":{"id":"2001.01211","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-01-05T11:12:08Z","cross_cats_sorted":[],"title_canon_sha256":"36e54ebf007e24cd2ee6c08d99e551610d6a6e0d76c2adb3a2a671ca0712697d","abstract_canon_sha256":"b2067a9977c57d53810644dd7f486a06adf3306361b8f664b0a392568ff3e85f"},"schema_version":"1.0"},"canonical_sha256":"25a85502ec6e861d22215a3e6b079ec61034f3734dd4c61b66aad5767e9f9d6e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:29:44.995950Z","signature_b64":"vC3oZGLcVEbOdRudhUv0kMM1WrVzaJsbD9QNsO6SIovLCCdAbA85/CBbPDyKgf67pUKaD8waxGTcFgaGoCZsAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"25a85502ec6e861d22215a3e6b079ec61034f3734dd4c61b66aad5767e9f9d6e","last_reissued_at":"2026-07-05T00:29:44.995524Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:29:44.995524Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2001.01211","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":"2026-07-05T00:29:44Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"i2oZ/zoVEV2Aey7rdd9Te6pMJ5EjLsNMtINrw39CrCk6nYkDHGSlWgQjS/kFsbONJLmcsvocVQMaO7sapTZdCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-09T05:03:59.356060Z"},"content_sha256":"a46c0766148a099632520a4fb82a94e449ac33c88abd15b3f67b75a631e72548","schema_version":"1.0","event_id":"sha256:a46c0766148a099632520a4fb82a94e449ac33c88abd15b3f67b75a631e72548"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:EWUFKAXMN2DB2IRBLI7GWB46YY","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Spatial-Scale Aligned Network for Fine-Grained Recognition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chong Sun, Haihua Xu, Junling Liu, Lizhao Gao, Yu-Wing Tai","submitted_at":"2020-01-05T11:12:08Z","abstract_excerpt":"Existing approaches for fine-grained visual recognition focus on learning marginal region-based representations while neglecting the spatial and scale misalignments, leading to inferior performance. In this paper, we propose the spatial-scale aligned network (SSANET) and implicitly address misalignments during the recognition process. Especially, SSANET consists of 1) a self-supervised proposal mining formula with Morphological Alignment Constraints; 2) a discriminative scale mining (DSM) module, which exploits the feature pyramid via a circulant matrix, and provides the Fourier solver for fas"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2001.01211","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2001.01211/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2026-07-05T00:29:44Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"kwHfi80fFpOIEhllzCF7hxwCT9xvvwXV+8N4r24FqvA3KKI44xxlM+Cjc4eNZB496PlgDyScSZQjadBTxc0jAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-09T05:03:59.356474Z"},"content_sha256":"354ee45c3ae40c1e1dfc848d4ae8262d1203a7f921e2a7682377daa0ec323df3","schema_version":"1.0","event_id":"sha256:354ee45c3ae40c1e1dfc848d4ae8262d1203a7f921e2a7682377daa0ec323df3"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/EWUFKAXMN2DB2IRBLI7GWB46YY/bundle.json","state_url":"https://pith.science/pith/EWUFKAXMN2DB2IRBLI7GWB46YY/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/EWUFKAXMN2DB2IRBLI7GWB46YY/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":"2026-07-09T05:03:59Z","links":{"resolver":"https://pith.science/pith/EWUFKAXMN2DB2IRBLI7GWB46YY","bundle":"https://pith.science/pith/EWUFKAXMN2DB2IRBLI7GWB46YY/bundle.json","state":"https://pith.science/pith/EWUFKAXMN2DB2IRBLI7GWB46YY/state.json","well_known_bundle":"https://pith.science/.well-known/pith/EWUFKAXMN2DB2IRBLI7GWB46YY/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:EWUFKAXMN2DB2IRBLI7GWB46YY","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":"b2067a9977c57d53810644dd7f486a06adf3306361b8f664b0a392568ff3e85f","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-01-05T11:12:08Z","title_canon_sha256":"36e54ebf007e24cd2ee6c08d99e551610d6a6e0d76c2adb3a2a671ca0712697d"},"schema_version":"1.0","source":{"id":"2001.01211","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2001.01211","created_at":"2026-07-05T00:29:44Z"},{"alias_kind":"arxiv_version","alias_value":"2001.01211v1","created_at":"2026-07-05T00:29:44Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2001.01211","created_at":"2026-07-05T00:29:44Z"},{"alias_kind":"pith_short_12","alias_value":"EWUFKAXMN2DB","created_at":"2026-07-05T00:29:44Z"},{"alias_kind":"pith_short_16","alias_value":"EWUFKAXMN2DB2IRB","created_at":"2026-07-05T00:29:44Z"},{"alias_kind":"pith_short_8","alias_value":"EWUFKAXM","created_at":"2026-07-05T00:29:44Z"}],"graph_snapshots":[{"event_id":"sha256:354ee45c3ae40c1e1dfc848d4ae8262d1203a7f921e2a7682377daa0ec323df3","target":"graph","created_at":"2026-07-05T00:29:44Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2001.01211/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Existing approaches for fine-grained visual recognition focus on learning marginal region-based representations while neglecting the spatial and scale misalignments, leading to inferior performance. In this paper, we propose the spatial-scale aligned network (SSANET) and implicitly address misalignments during the recognition process. Especially, SSANET consists of 1) a self-supervised proposal mining formula with Morphological Alignment Constraints; 2) a discriminative scale mining (DSM) module, which exploits the feature pyramid via a circulant matrix, and provides the Fourier solver for fas","authors_text":"Chong Sun, Haihua Xu, Junling Liu, Lizhao Gao, Yu-Wing Tai","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-01-05T11:12:08Z","title":"Spatial-Scale Aligned Network for Fine-Grained Recognition"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2001.01211","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:a46c0766148a099632520a4fb82a94e449ac33c88abd15b3f67b75a631e72548","target":"record","created_at":"2026-07-05T00:29:44Z","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":"b2067a9977c57d53810644dd7f486a06adf3306361b8f664b0a392568ff3e85f","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-01-05T11:12:08Z","title_canon_sha256":"36e54ebf007e24cd2ee6c08d99e551610d6a6e0d76c2adb3a2a671ca0712697d"},"schema_version":"1.0","source":{"id":"2001.01211","kind":"arxiv","version":1}},"canonical_sha256":"25a85502ec6e861d22215a3e6b079ec61034f3734dd4c61b66aad5767e9f9d6e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"25a85502ec6e861d22215a3e6b079ec61034f3734dd4c61b66aad5767e9f9d6e","first_computed_at":"2026-07-05T00:29:44.995524Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:29:44.995524Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"vC3oZGLcVEbOdRudhUv0kMM1WrVzaJsbD9QNsO6SIovLCCdAbA85/CBbPDyKgf67pUKaD8waxGTcFgaGoCZsAA==","signature_status":"signed_v1","signed_at":"2026-07-05T00:29:44.995950Z","signed_message":"canonical_sha256_bytes"},"source_id":"2001.01211","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a46c0766148a099632520a4fb82a94e449ac33c88abd15b3f67b75a631e72548","sha256:354ee45c3ae40c1e1dfc848d4ae8262d1203a7f921e2a7682377daa0ec323df3"],"state_sha256":"553582543b83d304ddbb541e99ad3d7df811c11be7c2f1dc578972af7100d2bb"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cjTEz+1K4yHpE6yqNW9I3WA9/6g8DyvgITELp1gdTsRpyChxM9FMRamab6KQ7SXXdrCYWXZutvqAOZQrG6BkBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-09T05:03:59.358463Z","bundle_sha256":"c9dc285cfe646b7b967d59174edbfcc90b9d4e80fd5675c0e263bdd8a5338699"}}