{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2015:6VZGPD5SCXZOCEQX23PRA2GMKD","short_pith_number":"pith:6VZGPD5S","canonical_record":{"source":{"id":"1511.06449","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-11-19T23:30:06Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"dd1bf3ebb6d0d6f58375a824db1d03566249db19db1224ed0bb4d3ac949949a8","abstract_canon_sha256":"e8470af0c43bd07ca21f2e4d5fb87f26bd7d4ea473603ea73951a339401d733a"},"schema_version":"1.0"},"canonical_sha256":"f572678fb215f2e11217d6df1068cc50f5bb00658eb9d9cebe54c5e14b82cce5","source":{"kind":"arxiv","id":"1511.06449","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1511.06449","created_at":"2026-05-18T01:15:06Z"},{"alias_kind":"arxiv_version","alias_value":"1511.06449v3","created_at":"2026-05-18T01:15:06Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1511.06449","created_at":"2026-05-18T01:15:06Z"},{"alias_kind":"pith_short_12","alias_value":"6VZGPD5SCXZO","created_at":"2026-05-18T12:29:07Z"},{"alias_kind":"pith_short_16","alias_value":"6VZGPD5SCXZOCEQX","created_at":"2026-05-18T12:29:07Z"},{"alias_kind":"pith_short_8","alias_value":"6VZGPD5S","created_at":"2026-05-18T12:29:07Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2015:6VZGPD5SCXZOCEQX23PRA2GMKD","target":"record","payload":{"canonical_record":{"source":{"id":"1511.06449","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-11-19T23:30:06Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"dd1bf3ebb6d0d6f58375a824db1d03566249db19db1224ed0bb4d3ac949949a8","abstract_canon_sha256":"e8470af0c43bd07ca21f2e4d5fb87f26bd7d4ea473603ea73951a339401d733a"},"schema_version":"1.0"},"canonical_sha256":"f572678fb215f2e11217d6df1068cc50f5bb00658eb9d9cebe54c5e14b82cce5","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:15:06.529207Z","signature_b64":"FpxGo9RdNdbrNgpPjxWKHcX/pd0CpOR3oHjahNvuJDjKFJgkO7bHiIfnji9JHfIcBfOhxzZXDGbJ2rcXurenDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f572678fb215f2e11217d6df1068cc50f5bb00658eb9d9cebe54c5e14b82cce5","last_reissued_at":"2026-05-18T01:15:06.528267Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:15:06.528267Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1511.06449","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T01:15:06Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"yJ24+uQ/WcLT8DtO6KOGm78//4cz9U1aTVuNtZEd/IWkfMoEQ62kkbrT2Xi8AysYiPhsqVr1NLcKMz19l69YAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-09T16:20:58.207088Z"},"content_sha256":"c98f9a81f8756e6dcca48875fd54806bd2549df0756d43643f61e8470d1b3720","schema_version":"1.0","event_id":"sha256:c98f9a81f8756e6dcca48875fd54806bd2549df0756d43643f61e8470d1b3720"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2015:6VZGPD5SCXZOCEQX23PRA2GMKD","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Learning to decompose for object detection and instance segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Alexander C. Berg, Eunbyung Park","submitted_at":"2015-11-19T23:30:06Z","abstract_excerpt":"Although deep convolutional neural networks(CNNs) have achieved remarkable results on object detection and segmentation, pre- and post-processing steps such as region proposals and non-maximum suppression(NMS), have been required. These steps result in high computational complexity and sensitivity to hyperparameters, e.g. thresholds for NMS. In this work, we propose a novel end-to-end trainable deep neural network architecture, which consists of convolutional and recurrent layers, that generates the correct number of object instances and their bounding boxes (or segmentation masks) given an im"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1511.06449","kind":"arxiv","version":3},"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":"2026-05-18T01:15:06Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Ax6Swcj3RBL14YjFZID6lVFm0/1LqPqTA3OnxKUJtPd76S/jfFPOdxlMYOTPa+pzu2F+uegaIARZNM4UkXhODw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-09T16:20:58.207456Z"},"content_sha256":"cbb7b768fe6dd89fe52afe2e64393b179e810d24d62b42fe96e143baeb87c1e5","schema_version":"1.0","event_id":"sha256:cbb7b768fe6dd89fe52afe2e64393b179e810d24d62b42fe96e143baeb87c1e5"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/6VZGPD5SCXZOCEQX23PRA2GMKD/bundle.json","state_url":"https://pith.science/pith/6VZGPD5SCXZOCEQX23PRA2GMKD/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/6VZGPD5SCXZOCEQX23PRA2GMKD/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":"2026-06-09T16:20:58Z","links":{"resolver":"https://pith.science/pith/6VZGPD5SCXZOCEQX23PRA2GMKD","bundle":"https://pith.science/pith/6VZGPD5SCXZOCEQX23PRA2GMKD/bundle.json","state":"https://pith.science/pith/6VZGPD5SCXZOCEQX23PRA2GMKD/state.json","well_known_bundle":"https://pith.science/.well-known/pith/6VZGPD5SCXZOCEQX23PRA2GMKD/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2015:6VZGPD5SCXZOCEQX23PRA2GMKD","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":"e8470af0c43bd07ca21f2e4d5fb87f26bd7d4ea473603ea73951a339401d733a","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-11-19T23:30:06Z","title_canon_sha256":"dd1bf3ebb6d0d6f58375a824db1d03566249db19db1224ed0bb4d3ac949949a8"},"schema_version":"1.0","source":{"id":"1511.06449","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1511.06449","created_at":"2026-05-18T01:15:06Z"},{"alias_kind":"arxiv_version","alias_value":"1511.06449v3","created_at":"2026-05-18T01:15:06Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1511.06449","created_at":"2026-05-18T01:15:06Z"},{"alias_kind":"pith_short_12","alias_value":"6VZGPD5SCXZO","created_at":"2026-05-18T12:29:07Z"},{"alias_kind":"pith_short_16","alias_value":"6VZGPD5SCXZOCEQX","created_at":"2026-05-18T12:29:07Z"},{"alias_kind":"pith_short_8","alias_value":"6VZGPD5S","created_at":"2026-05-18T12:29:07Z"}],"graph_snapshots":[{"event_id":"sha256:cbb7b768fe6dd89fe52afe2e64393b179e810d24d62b42fe96e143baeb87c1e5","target":"graph","created_at":"2026-05-18T01:15:06Z","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":"Although deep convolutional neural networks(CNNs) have achieved remarkable results on object detection and segmentation, pre- and post-processing steps such as region proposals and non-maximum suppression(NMS), have been required. These steps result in high computational complexity and sensitivity to hyperparameters, e.g. thresholds for NMS. In this work, we propose a novel end-to-end trainable deep neural network architecture, which consists of convolutional and recurrent layers, that generates the correct number of object instances and their bounding boxes (or segmentation masks) given an im","authors_text":"Alexander C. Berg, Eunbyung Park","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-11-19T23:30:06Z","title":"Learning to decompose for object detection and instance segmentation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1511.06449","kind":"arxiv","version":3},"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:c98f9a81f8756e6dcca48875fd54806bd2549df0756d43643f61e8470d1b3720","target":"record","created_at":"2026-05-18T01:15:06Z","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":"e8470af0c43bd07ca21f2e4d5fb87f26bd7d4ea473603ea73951a339401d733a","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-11-19T23:30:06Z","title_canon_sha256":"dd1bf3ebb6d0d6f58375a824db1d03566249db19db1224ed0bb4d3ac949949a8"},"schema_version":"1.0","source":{"id":"1511.06449","kind":"arxiv","version":3}},"canonical_sha256":"f572678fb215f2e11217d6df1068cc50f5bb00658eb9d9cebe54c5e14b82cce5","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f572678fb215f2e11217d6df1068cc50f5bb00658eb9d9cebe54c5e14b82cce5","first_computed_at":"2026-05-18T01:15:06.528267Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:15:06.528267Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"FpxGo9RdNdbrNgpPjxWKHcX/pd0CpOR3oHjahNvuJDjKFJgkO7bHiIfnji9JHfIcBfOhxzZXDGbJ2rcXurenDw==","signature_status":"signed_v1","signed_at":"2026-05-18T01:15:06.529207Z","signed_message":"canonical_sha256_bytes"},"source_id":"1511.06449","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c98f9a81f8756e6dcca48875fd54806bd2549df0756d43643f61e8470d1b3720","sha256:cbb7b768fe6dd89fe52afe2e64393b179e810d24d62b42fe96e143baeb87c1e5"],"state_sha256":"7c24d1f9526a0072d7b1bf30d81acf0ed9ec109b3b59f2c7b2b0a46a007a185e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wvqr4TcZcZl37nWbaPqi4uOHrItNPZx/TJvjgYzOLJ/bqWxO6Ow14rHmQSFwFobBUErLcmqCNLju0g/7vvUkAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-09T16:20:58.209513Z","bundle_sha256":"8b69d2e77760204761ba127f2be05dfc012d259b430f1ec46ecd3a3713700283"}}