{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:SJ5VX4WL4RM7DFH45CRCHHSQ2K","short_pith_number":"pith:SJ5VX4WL","canonical_record":{"source":{"id":"1712.07465","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-12-20T13:14:46Z","cross_cats_sorted":[],"title_canon_sha256":"5e686c12b4addc0bd88883b9b17c563069cf0247bc71f703cb312e6072b263ba","abstract_canon_sha256":"61369f29b346873e1f35d2a592107549be0875107fc84cce118937385d1b348d"},"schema_version":"1.0"},"canonical_sha256":"927b5bf2cbe459f194fce8a2239e50d2a19c151e9d38dc2520f00be937919771","source":{"kind":"arxiv","id":"1712.07465","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1712.07465","created_at":"2026-05-18T00:27:34Z"},{"alias_kind":"arxiv_version","alias_value":"1712.07465v1","created_at":"2026-05-18T00:27:34Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1712.07465","created_at":"2026-05-18T00:27:34Z"},{"alias_kind":"pith_short_12","alias_value":"SJ5VX4WL4RM7","created_at":"2026-05-18T12:31:43Z"},{"alias_kind":"pith_short_16","alias_value":"SJ5VX4WL4RM7DFH4","created_at":"2026-05-18T12:31:43Z"},{"alias_kind":"pith_short_8","alias_value":"SJ5VX4WL","created_at":"2026-05-18T12:31:43Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:SJ5VX4WL4RM7DFH45CRCHHSQ2K","target":"record","payload":{"canonical_record":{"source":{"id":"1712.07465","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-12-20T13:14:46Z","cross_cats_sorted":[],"title_canon_sha256":"5e686c12b4addc0bd88883b9b17c563069cf0247bc71f703cb312e6072b263ba","abstract_canon_sha256":"61369f29b346873e1f35d2a592107549be0875107fc84cce118937385d1b348d"},"schema_version":"1.0"},"canonical_sha256":"927b5bf2cbe459f194fce8a2239e50d2a19c151e9d38dc2520f00be937919771","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:27:34.457879Z","signature_b64":"A+4eVjLB5NHDvphYDndxtsW/8fA5BcKHllPPx4x8ZIHroTHnVi0wUUBbuhirT63X0gvrM3gurJ9Qj810opRkAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"927b5bf2cbe459f194fce8a2239e50d2a19c151e9d38dc2520f00be937919771","last_reissued_at":"2026-05-18T00:27:34.457347Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:27:34.457347Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1712.07465","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":"2026-05-18T00:27:34Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"bNNzHWI1N4sV/1V8HkjsnwoH7IAJIntoRDYa3ZF8v5LUgDpBKfYu+xEoTmVHZkfDRgjKCGNCrsZoQBs6WzwdAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-08T21:21:06.245200Z"},"content_sha256":"883a7fc6240093eafc97ca5c09476b1b2c9cb61e8ba2974827d4dad690d38ef9","schema_version":"1.0","event_id":"sha256:883a7fc6240093eafc97ca5c09476b1b2c9cb61e8ba2974827d4dad690d38ef9"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:SJ5VX4WL4RM7DFH45CRCHHSQ2K","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Recurrent Attentional Reinforcement Learning for Multi-label Image Recognition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Guanbin Li, Liang Lin, Tianshui Chen, Zhouxia Wang","submitted_at":"2017-12-20T13:14:46Z","abstract_excerpt":"Recognizing multiple labels of images is a fundamental but challenging task in computer vision, and remarkable progress has been attained by localizing semantic-aware image regions and predicting their labels with deep convolutional neural networks. The step of hypothesis regions (region proposals) localization in these existing multi-label image recognition pipelines, however, usually takes redundant computation cost, e.g., generating hundreds of meaningless proposals with non-discriminative information and extracting their features, and the spatial contextual dependency modeling among the lo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1712.07465","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":"2026-05-18T00:27:34Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wuUDuHXngHfYNspRt4zZ6/UgNu0kIsQNfJ8mQlyOzlKJvh81IxtuCyl02v9dkI0+DLUXnJdhc+zw3iwghvwRDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-08T21:21:06.245956Z"},"content_sha256":"8bcee7934200b23611c5efca051268bc338b401c83950ff409622df47e1f84e6","schema_version":"1.0","event_id":"sha256:8bcee7934200b23611c5efca051268bc338b401c83950ff409622df47e1f84e6"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/SJ5VX4WL4RM7DFH45CRCHHSQ2K/bundle.json","state_url":"https://pith.science/pith/SJ5VX4WL4RM7DFH45CRCHHSQ2K/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/SJ5VX4WL4RM7DFH45CRCHHSQ2K/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":"2026-06-08T21:21:06Z","links":{"resolver":"https://pith.science/pith/SJ5VX4WL4RM7DFH45CRCHHSQ2K","bundle":"https://pith.science/pith/SJ5VX4WL4RM7DFH45CRCHHSQ2K/bundle.json","state":"https://pith.science/pith/SJ5VX4WL4RM7DFH45CRCHHSQ2K/state.json","well_known_bundle":"https://pith.science/.well-known/pith/SJ5VX4WL4RM7DFH45CRCHHSQ2K/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:SJ5VX4WL4RM7DFH45CRCHHSQ2K","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":"61369f29b346873e1f35d2a592107549be0875107fc84cce118937385d1b348d","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-12-20T13:14:46Z","title_canon_sha256":"5e686c12b4addc0bd88883b9b17c563069cf0247bc71f703cb312e6072b263ba"},"schema_version":"1.0","source":{"id":"1712.07465","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1712.07465","created_at":"2026-05-18T00:27:34Z"},{"alias_kind":"arxiv_version","alias_value":"1712.07465v1","created_at":"2026-05-18T00:27:34Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1712.07465","created_at":"2026-05-18T00:27:34Z"},{"alias_kind":"pith_short_12","alias_value":"SJ5VX4WL4RM7","created_at":"2026-05-18T12:31:43Z"},{"alias_kind":"pith_short_16","alias_value":"SJ5VX4WL4RM7DFH4","created_at":"2026-05-18T12:31:43Z"},{"alias_kind":"pith_short_8","alias_value":"SJ5VX4WL","created_at":"2026-05-18T12:31:43Z"}],"graph_snapshots":[{"event_id":"sha256:8bcee7934200b23611c5efca051268bc338b401c83950ff409622df47e1f84e6","target":"graph","created_at":"2026-05-18T00:27:34Z","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":"Recognizing multiple labels of images is a fundamental but challenging task in computer vision, and remarkable progress has been attained by localizing semantic-aware image regions and predicting their labels with deep convolutional neural networks. The step of hypothesis regions (region proposals) localization in these existing multi-label image recognition pipelines, however, usually takes redundant computation cost, e.g., generating hundreds of meaningless proposals with non-discriminative information and extracting their features, and the spatial contextual dependency modeling among the lo","authors_text":"Guanbin Li, Liang Lin, Tianshui Chen, Zhouxia Wang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-12-20T13:14:46Z","title":"Recurrent Attentional Reinforcement Learning for Multi-label Image Recognition"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1712.07465","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":"sha256:883a7fc6240093eafc97ca5c09476b1b2c9cb61e8ba2974827d4dad690d38ef9","target":"record","created_at":"2026-05-18T00:27:34Z","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":"61369f29b346873e1f35d2a592107549be0875107fc84cce118937385d1b348d","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-12-20T13:14:46Z","title_canon_sha256":"5e686c12b4addc0bd88883b9b17c563069cf0247bc71f703cb312e6072b263ba"},"schema_version":"1.0","source":{"id":"1712.07465","kind":"arxiv","version":1}},"canonical_sha256":"927b5bf2cbe459f194fce8a2239e50d2a19c151e9d38dc2520f00be937919771","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"927b5bf2cbe459f194fce8a2239e50d2a19c151e9d38dc2520f00be937919771","first_computed_at":"2026-05-18T00:27:34.457347Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:27:34.457347Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"A+4eVjLB5NHDvphYDndxtsW/8fA5BcKHllPPx4x8ZIHroTHnVi0wUUBbuhirT63X0gvrM3gurJ9Qj810opRkAw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:27:34.457879Z","signed_message":"canonical_sha256_bytes"},"source_id":"1712.07465","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:883a7fc6240093eafc97ca5c09476b1b2c9cb61e8ba2974827d4dad690d38ef9","sha256:8bcee7934200b23611c5efca051268bc338b401c83950ff409622df47e1f84e6"],"state_sha256":"54f7851e2b1316419f0de5ce1fb83aeb8af17f0b76998121b7830e9e0ebacd91"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"dNHA7xPeJPdVJ0YESb2Y5mvt8dd/3m8LE8BjRhOGaMKDtibl7aBlJlN+GjDIhSAX9bYy7MvrAoqigCySVdYlDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-08T21:21:06.250221Z","bundle_sha256":"739f393741f3caab0984f149a8228326f056c7f1fb81df422c1b7c2ca3962431"}}