{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:UZDWAN6OUGEW52IFRYGNWG2LGK","short_pith_number":"pith:UZDWAN6O","canonical_record":{"source":{"id":"1604.03239","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-04-12T03:57:48Z","cross_cats_sorted":[],"title_canon_sha256":"d86c8a0125a3720e8f7677b4051731b99ac73393b1d82da745a45de3b6a1a201","abstract_canon_sha256":"20b90b602928f183b806fc406bf19573c1be96197acde759ebb9a191f4a18744"},"schema_version":"1.0"},"canonical_sha256":"a6476037cea1896ee9058e0cdb1b4b32992bbc5cd6fb37f4999a0c84e731da37","source":{"kind":"arxiv","id":"1604.03239","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1604.03239","created_at":"2026-05-18T01:17:15Z"},{"alias_kind":"arxiv_version","alias_value":"1604.03239v1","created_at":"2026-05-18T01:17:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1604.03239","created_at":"2026-05-18T01:17:15Z"},{"alias_kind":"pith_short_12","alias_value":"UZDWAN6OUGEW","created_at":"2026-05-18T12:30:46Z"},{"alias_kind":"pith_short_16","alias_value":"UZDWAN6OUGEW52IF","created_at":"2026-05-18T12:30:46Z"},{"alias_kind":"pith_short_8","alias_value":"UZDWAN6O","created_at":"2026-05-18T12:30:46Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:UZDWAN6OUGEW52IFRYGNWG2LGK","target":"record","payload":{"canonical_record":{"source":{"id":"1604.03239","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-04-12T03:57:48Z","cross_cats_sorted":[],"title_canon_sha256":"d86c8a0125a3720e8f7677b4051731b99ac73393b1d82da745a45de3b6a1a201","abstract_canon_sha256":"20b90b602928f183b806fc406bf19573c1be96197acde759ebb9a191f4a18744"},"schema_version":"1.0"},"canonical_sha256":"a6476037cea1896ee9058e0cdb1b4b32992bbc5cd6fb37f4999a0c84e731da37","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:17:15.983021Z","signature_b64":"9yptDe5XiTTwK4SHlwcsYY0YUjcfH6Xc0x57D5aJkt9VO7hfbvZd2LWn8A7QuGmMSGPwTIuGmPNg/uRpau8nBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a6476037cea1896ee9058e0cdb1b4b32992bbc5cd6fb37f4999a0c84e731da37","last_reissued_at":"2026-05-18T01:17:15.982364Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:17:15.982364Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1604.03239","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":"2026-05-18T01:17:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"IkjQgoJiLCDiv/pc7zOfxFkapmy2Tc20SjDKQzK13bUajvyBROBFrnxUzg7PI7WX+IRLLa7QpvjdhU42OxdjCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-04T20:54:35.346412Z"},"content_sha256":"97bb56eb8a7d4affe224984ee161f5716b1b058df1afd9f6564121dc31cd3acf","schema_version":"1.0","event_id":"sha256:97bb56eb8a7d4affe224984ee161f5716b1b058df1afd9f6564121dc31cd3acf"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:UZDWAN6OUGEW52IFRYGNWG2LGK","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"CRAFT Objects from Images","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bin Yang, Junjie Yan, Stan Z. Li, Zhen Lei","submitted_at":"2016-04-12T03:57:48Z","abstract_excerpt":"Object detection is a fundamental problem in image understanding. One popular solution is the R-CNN framework and its fast versions. They decompose the object detection problem into two cascaded easier tasks: 1) generating object proposals from images, 2) classifying proposals into various object categories. Despite that we are handling with two relatively easier tasks, they are not solved perfectly and there's still room for improvement. In this paper, we push the \"divide and conquer\" solution even further by dividing each task into two sub-tasks. We call the proposed method \"CRAFT\" (Cascade "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1604.03239","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":"2026-05-18T01:17:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"v/Rz5/xViBIhITfDizgkr316Rstbx33pxDnh9QHWr0bAqpGzuM8oXF0NGW9FTPZagSvauKC5JoqYvgl5yCN0Cw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-04T20:54:35.347071Z"},"content_sha256":"ff856521588aaf0eea6681fbe557db41f04ebfe713d310498e2835abdd78421f","schema_version":"1.0","event_id":"sha256:ff856521588aaf0eea6681fbe557db41f04ebfe713d310498e2835abdd78421f"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/UZDWAN6OUGEW52IFRYGNWG2LGK/bundle.json","state_url":"https://pith.science/pith/UZDWAN6OUGEW52IFRYGNWG2LGK/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/UZDWAN6OUGEW52IFRYGNWG2LGK/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":"2026-06-04T20:54:35Z","links":{"resolver":"https://pith.science/pith/UZDWAN6OUGEW52IFRYGNWG2LGK","bundle":"https://pith.science/pith/UZDWAN6OUGEW52IFRYGNWG2LGK/bundle.json","state":"https://pith.science/pith/UZDWAN6OUGEW52IFRYGNWG2LGK/state.json","well_known_bundle":"https://pith.science/.well-known/pith/UZDWAN6OUGEW52IFRYGNWG2LGK/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:UZDWAN6OUGEW52IFRYGNWG2LGK","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":"20b90b602928f183b806fc406bf19573c1be96197acde759ebb9a191f4a18744","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-04-12T03:57:48Z","title_canon_sha256":"d86c8a0125a3720e8f7677b4051731b99ac73393b1d82da745a45de3b6a1a201"},"schema_version":"1.0","source":{"id":"1604.03239","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1604.03239","created_at":"2026-05-18T01:17:15Z"},{"alias_kind":"arxiv_version","alias_value":"1604.03239v1","created_at":"2026-05-18T01:17:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1604.03239","created_at":"2026-05-18T01:17:15Z"},{"alias_kind":"pith_short_12","alias_value":"UZDWAN6OUGEW","created_at":"2026-05-18T12:30:46Z"},{"alias_kind":"pith_short_16","alias_value":"UZDWAN6OUGEW52IF","created_at":"2026-05-18T12:30:46Z"},{"alias_kind":"pith_short_8","alias_value":"UZDWAN6O","created_at":"2026-05-18T12:30:46Z"}],"graph_snapshots":[{"event_id":"sha256:ff856521588aaf0eea6681fbe557db41f04ebfe713d310498e2835abdd78421f","target":"graph","created_at":"2026-05-18T01:17:15Z","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":"Object detection is a fundamental problem in image understanding. One popular solution is the R-CNN framework and its fast versions. They decompose the object detection problem into two cascaded easier tasks: 1) generating object proposals from images, 2) classifying proposals into various object categories. Despite that we are handling with two relatively easier tasks, they are not solved perfectly and there's still room for improvement. In this paper, we push the \"divide and conquer\" solution even further by dividing each task into two sub-tasks. We call the proposed method \"CRAFT\" (Cascade ","authors_text":"Bin Yang, Junjie Yan, Stan Z. Li, Zhen Lei","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-04-12T03:57:48Z","title":"CRAFT Objects from Images"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1604.03239","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":"sha256:97bb56eb8a7d4affe224984ee161f5716b1b058df1afd9f6564121dc31cd3acf","target":"record","created_at":"2026-05-18T01:17:15Z","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":"20b90b602928f183b806fc406bf19573c1be96197acde759ebb9a191f4a18744","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-04-12T03:57:48Z","title_canon_sha256":"d86c8a0125a3720e8f7677b4051731b99ac73393b1d82da745a45de3b6a1a201"},"schema_version":"1.0","source":{"id":"1604.03239","kind":"arxiv","version":1}},"canonical_sha256":"a6476037cea1896ee9058e0cdb1b4b32992bbc5cd6fb37f4999a0c84e731da37","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a6476037cea1896ee9058e0cdb1b4b32992bbc5cd6fb37f4999a0c84e731da37","first_computed_at":"2026-05-18T01:17:15.982364Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:17:15.982364Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"9yptDe5XiTTwK4SHlwcsYY0YUjcfH6Xc0x57D5aJkt9VO7hfbvZd2LWn8A7QuGmMSGPwTIuGmPNg/uRpau8nBw==","signature_status":"signed_v1","signed_at":"2026-05-18T01:17:15.983021Z","signed_message":"canonical_sha256_bytes"},"source_id":"1604.03239","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:97bb56eb8a7d4affe224984ee161f5716b1b058df1afd9f6564121dc31cd3acf","sha256:ff856521588aaf0eea6681fbe557db41f04ebfe713d310498e2835abdd78421f"],"state_sha256":"4d47896bf5eb4ec14ee983e843a382cb336dfcbcef0f0b0caa6a9854e8a327f3"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"URxMP6jXyX1157rydz+YbQtouda1w9o2mMhtAZjys8K3lb/6mERurAh8CxIQ/L+KCLkYztOSlq8zMVBSai7WAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-04T20:54:35.350665Z","bundle_sha256":"b67fc317da83c040412c80829ae23058f7b217e435c1b11b4688860053e19371"}}