{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:WXFW7FRN22FHS6YFT7VFB4JYGV","short_pith_number":"pith:WXFW7FRN","canonical_record":{"source":{"id":"1608.01471","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-08-04T09:06:15Z","cross_cats_sorted":[],"title_canon_sha256":"6c3d3c13210118a151d049eb222e5df289087712d1b195fb2e2b23c90802d4fa","abstract_canon_sha256":"df2f25b45a87018b0458959b1bda1a94b5a4ab06d9574a7d2ec72f80fa6096f3"},"schema_version":"1.0"},"canonical_sha256":"b5cb6f962dd68a797b059fea50f1383552b5a208e59039a4bd1f59adc6f927c7","source":{"kind":"arxiv","id":"1608.01471","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1608.01471","created_at":"2026-05-18T01:09:52Z"},{"alias_kind":"arxiv_version","alias_value":"1608.01471v1","created_at":"2026-05-18T01:09:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1608.01471","created_at":"2026-05-18T01:09:52Z"},{"alias_kind":"pith_short_12","alias_value":"WXFW7FRN22FH","created_at":"2026-05-18T12:30:51Z"},{"alias_kind":"pith_short_16","alias_value":"WXFW7FRN22FHS6YF","created_at":"2026-05-18T12:30:51Z"},{"alias_kind":"pith_short_8","alias_value":"WXFW7FRN","created_at":"2026-05-18T12:30:51Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:WXFW7FRN22FHS6YFT7VFB4JYGV","target":"record","payload":{"canonical_record":{"source":{"id":"1608.01471","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-08-04T09:06:15Z","cross_cats_sorted":[],"title_canon_sha256":"6c3d3c13210118a151d049eb222e5df289087712d1b195fb2e2b23c90802d4fa","abstract_canon_sha256":"df2f25b45a87018b0458959b1bda1a94b5a4ab06d9574a7d2ec72f80fa6096f3"},"schema_version":"1.0"},"canonical_sha256":"b5cb6f962dd68a797b059fea50f1383552b5a208e59039a4bd1f59adc6f927c7","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:09:52.353687Z","signature_b64":"Sef/wfrryjkhHdWqkFtzf6rrhD/EiQYt7RIfP65JKvn1dLHY4rjUfCWiaA06ktwUj3VQIZF1P8ReykWxnpbEDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b5cb6f962dd68a797b059fea50f1383552b5a208e59039a4bd1f59adc6f927c7","last_reissued_at":"2026-05-18T01:09:52.353046Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:09:52.353046Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1608.01471","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":"2026-05-18T01:09:52Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"JgP609YIn7a3HysWJj+uMzSO5mi92UFnnHJR4MDLJp8ank/Dv+zbVei+IEBq8N7e8oj9/mV/y2tr6vsVA5I/Dw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T11:36:45.337245Z"},"content_sha256":"ebc408af317aa637588df6a8baf882944db9b115d493acea5f870232918f97aa","schema_version":"1.0","event_id":"sha256:ebc408af317aa637588df6a8baf882944db9b115d493acea5f870232918f97aa"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:WXFW7FRN22FHS6YFT7VFB4JYGV","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"UnitBox: An Advanced Object Detection Network","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jiahui Yu, Thomas Huang, Yuning Jiang, Zhangyang Wang, Zhimin Cao","submitted_at":"2016-08-04T09:06:15Z","abstract_excerpt":"In present object detection systems, the deep convolutional neural networks (CNNs) are utilized to predict bounding boxes of object candidates, and have gained performance advantages over the traditional region proposal methods. However, existing deep CNN methods assume the object bounds to be four independent variables, which could be regressed by the $\\ell_2$ loss separately. Such an oversimplified assumption is contrary to the well-received observation, that those variables are correlated, resulting to less accurate localization. To address the issue, we firstly introduce a novel Intersecti"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1608.01471","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":"2026-05-18T01:09:52Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"hgmwW3scqrstDEvzmB89uaPtK2KARhQtjRv8EchODms/7V9GjmDeeH1rkRdS/v2DgGlzA6jmj6zMSqWiWvu2Bw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T11:36:45.337605Z"},"content_sha256":"36bc8a41286582fbf8dc111f6be9bfce869d3a0623b7bd52590e09893f7e6633","schema_version":"1.0","event_id":"sha256:36bc8a41286582fbf8dc111f6be9bfce869d3a0623b7bd52590e09893f7e6633"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/WXFW7FRN22FHS6YFT7VFB4JYGV/bundle.json","state_url":"https://pith.science/pith/WXFW7FRN22FHS6YFT7VFB4JYGV/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/WXFW7FRN22FHS6YFT7VFB4JYGV/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":"2026-05-26T11:36:45Z","links":{"resolver":"https://pith.science/pith/WXFW7FRN22FHS6YFT7VFB4JYGV","bundle":"https://pith.science/pith/WXFW7FRN22FHS6YFT7VFB4JYGV/bundle.json","state":"https://pith.science/pith/WXFW7FRN22FHS6YFT7VFB4JYGV/state.json","well_known_bundle":"https://pith.science/.well-known/pith/WXFW7FRN22FHS6YFT7VFB4JYGV/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:WXFW7FRN22FHS6YFT7VFB4JYGV","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":"df2f25b45a87018b0458959b1bda1a94b5a4ab06d9574a7d2ec72f80fa6096f3","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-08-04T09:06:15Z","title_canon_sha256":"6c3d3c13210118a151d049eb222e5df289087712d1b195fb2e2b23c90802d4fa"},"schema_version":"1.0","source":{"id":"1608.01471","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1608.01471","created_at":"2026-05-18T01:09:52Z"},{"alias_kind":"arxiv_version","alias_value":"1608.01471v1","created_at":"2026-05-18T01:09:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1608.01471","created_at":"2026-05-18T01:09:52Z"},{"alias_kind":"pith_short_12","alias_value":"WXFW7FRN22FH","created_at":"2026-05-18T12:30:51Z"},{"alias_kind":"pith_short_16","alias_value":"WXFW7FRN22FHS6YF","created_at":"2026-05-18T12:30:51Z"},{"alias_kind":"pith_short_8","alias_value":"WXFW7FRN","created_at":"2026-05-18T12:30:51Z"}],"graph_snapshots":[{"event_id":"sha256:36bc8a41286582fbf8dc111f6be9bfce869d3a0623b7bd52590e09893f7e6633","target":"graph","created_at":"2026-05-18T01:09:52Z","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":"In present object detection systems, the deep convolutional neural networks (CNNs) are utilized to predict bounding boxes of object candidates, and have gained performance advantages over the traditional region proposal methods. However, existing deep CNN methods assume the object bounds to be four independent variables, which could be regressed by the $\\ell_2$ loss separately. Such an oversimplified assumption is contrary to the well-received observation, that those variables are correlated, resulting to less accurate localization. To address the issue, we firstly introduce a novel Intersecti","authors_text":"Jiahui Yu, Thomas Huang, Yuning Jiang, Zhangyang Wang, Zhimin Cao","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-08-04T09:06:15Z","title":"UnitBox: An Advanced Object Detection Network"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1608.01471","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:ebc408af317aa637588df6a8baf882944db9b115d493acea5f870232918f97aa","target":"record","created_at":"2026-05-18T01:09:52Z","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":"df2f25b45a87018b0458959b1bda1a94b5a4ab06d9574a7d2ec72f80fa6096f3","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-08-04T09:06:15Z","title_canon_sha256":"6c3d3c13210118a151d049eb222e5df289087712d1b195fb2e2b23c90802d4fa"},"schema_version":"1.0","source":{"id":"1608.01471","kind":"arxiv","version":1}},"canonical_sha256":"b5cb6f962dd68a797b059fea50f1383552b5a208e59039a4bd1f59adc6f927c7","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b5cb6f962dd68a797b059fea50f1383552b5a208e59039a4bd1f59adc6f927c7","first_computed_at":"2026-05-18T01:09:52.353046Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:09:52.353046Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Sef/wfrryjkhHdWqkFtzf6rrhD/EiQYt7RIfP65JKvn1dLHY4rjUfCWiaA06ktwUj3VQIZF1P8ReykWxnpbEDA==","signature_status":"signed_v1","signed_at":"2026-05-18T01:09:52.353687Z","signed_message":"canonical_sha256_bytes"},"source_id":"1608.01471","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ebc408af317aa637588df6a8baf882944db9b115d493acea5f870232918f97aa","sha256:36bc8a41286582fbf8dc111f6be9bfce869d3a0623b7bd52590e09893f7e6633"],"state_sha256":"b3ef69b274c0059e271daf3f5e6295ffb337abfbacaaa15311689985a1a07065"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"MjlTjDPB+Tr3WY7UKCOID+Iy6xhy1BP4gY7C0k+cm6Rix2hgg88tGOb2tJfZLKq5AqDs1+q5lE3E4kPr0+0VAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-26T11:36:45.339726Z","bundle_sha256":"23bb425a25826acd631f454b911d7d8042c4a2140cae86977e520e285b4c19d8"}}