{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:5FH6HBGWGWPB3GRYKRJYCFGWE7","short_pith_number":"pith:5FH6HBGW","canonical_record":{"source":{"id":"2205.12740","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-05-25T12:46:21Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"396b04b5374714e59f52d2756e469f3403d5a6c5847c0563ff33bd337b1deb93","abstract_canon_sha256":"687ffd4a0473ce9eb571c745c70ad5809576613b2d84c515d9c686e4b4555edc"},"schema_version":"1.0"},"canonical_sha256":"e94fe384d6359e1d9a3854538114d627fbec81933e7a4d07217ee3b84f82b820","source":{"kind":"arxiv","id":"2205.12740","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2205.12740","created_at":"2026-07-05T04:26:29Z"},{"alias_kind":"arxiv_version","alias_value":"2205.12740v1","created_at":"2026-07-05T04:26:29Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2205.12740","created_at":"2026-07-05T04:26:29Z"},{"alias_kind":"pith_short_12","alias_value":"5FH6HBGWGWPB","created_at":"2026-07-05T04:26:29Z"},{"alias_kind":"pith_short_16","alias_value":"5FH6HBGWGWPB3GRY","created_at":"2026-07-05T04:26:29Z"},{"alias_kind":"pith_short_8","alias_value":"5FH6HBGW","created_at":"2026-07-05T04:26:29Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:5FH6HBGWGWPB3GRYKRJYCFGWE7","target":"record","payload":{"canonical_record":{"source":{"id":"2205.12740","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-05-25T12:46:21Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"396b04b5374714e59f52d2756e469f3403d5a6c5847c0563ff33bd337b1deb93","abstract_canon_sha256":"687ffd4a0473ce9eb571c745c70ad5809576613b2d84c515d9c686e4b4555edc"},"schema_version":"1.0"},"canonical_sha256":"e94fe384d6359e1d9a3854538114d627fbec81933e7a4d07217ee3b84f82b820","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:26:29.414313Z","signature_b64":"EI6/dgTyKBdAOMcxYwBibV42PdICyY3KdozO3V554u839DFbjxsefjpykW093huiHLMT5Ogj+Gy/lbuaGiOdAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e94fe384d6359e1d9a3854538114d627fbec81933e7a4d07217ee3b84f82b820","last_reissued_at":"2026-07-05T04:26:29.413896Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:26:29.413896Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2205.12740","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":"2026-07-05T04:26:29Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"dchdAxsqibczI1HYQUJQqUDQcA6o8rCUj5r6z3WNeY0WJJnxfRJKT1ns0Kg2Obk5ByC/pBcroeLsj9fHXikwCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T02:15:40.514587Z"},"content_sha256":"dacbe62e1d437d6f00d6fac70b00f16ffcd21ef97d840431b85b3c3dc591bde5","schema_version":"1.0","event_id":"sha256:dacbe62e1d437d6f00d6fac70b00f16ffcd21ef97d840431b85b3c3dc591bde5"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:5FH6HBGWGWPB3GRYKRJYCFGWE7","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"SIoU Loss: More Powerful Learning for Bounding Box Regression","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Zhora Gevorgyan","submitted_at":"2022-05-25T12:46:21Z","abstract_excerpt":"The effectiveness of Object Detection, one of the central problems in computer vision tasks, highly depends on the definition of the loss function - a measure of how accurately your ML model can predict the expected outcome. Conventional object detection loss functions depend on aggregation of metrics of bounding box regression such as the distance, overlap area and aspect ratio of the predicted and ground truth boxes (i.e. GIoU, CIoU, ICIoU etc). However, none of the methods proposed and used to date considers the direction of the mismatch between the desired ground box and the predicted, \"ex"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2205.12740","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/2205.12740/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":"2026-07-05T04:26:29Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"TxPmrgJEShinHfyjXWuZTYP9hT2vcYKaYG1xrMIrESyNgJwlr02YgDNLBJXO5GS+McmCEBoY40HxETRUCKDECQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T02:15:40.515131Z"},"content_sha256":"c44b5df6a2f16cc85f2da6c40c4405a154dc748400c0b4f620a8b79f17c66f06","schema_version":"1.0","event_id":"sha256:c44b5df6a2f16cc85f2da6c40c4405a154dc748400c0b4f620a8b79f17c66f06"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/5FH6HBGWGWPB3GRYKRJYCFGWE7/bundle.json","state_url":"https://pith.science/pith/5FH6HBGWGWPB3GRYKRJYCFGWE7/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/5FH6HBGWGWPB3GRYKRJYCFGWE7/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":"2026-08-10T02:15:40Z","links":{"resolver":"https://pith.science/pith/5FH6HBGWGWPB3GRYKRJYCFGWE7","bundle":"https://pith.science/pith/5FH6HBGWGWPB3GRYKRJYCFGWE7/bundle.json","state":"https://pith.science/pith/5FH6HBGWGWPB3GRYKRJYCFGWE7/state.json","well_known_bundle":"https://pith.science/.well-known/pith/5FH6HBGWGWPB3GRYKRJYCFGWE7/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:5FH6HBGWGWPB3GRYKRJYCFGWE7","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":"687ffd4a0473ce9eb571c745c70ad5809576613b2d84c515d9c686e4b4555edc","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-05-25T12:46:21Z","title_canon_sha256":"396b04b5374714e59f52d2756e469f3403d5a6c5847c0563ff33bd337b1deb93"},"schema_version":"1.0","source":{"id":"2205.12740","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2205.12740","created_at":"2026-07-05T04:26:29Z"},{"alias_kind":"arxiv_version","alias_value":"2205.12740v1","created_at":"2026-07-05T04:26:29Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2205.12740","created_at":"2026-07-05T04:26:29Z"},{"alias_kind":"pith_short_12","alias_value":"5FH6HBGWGWPB","created_at":"2026-07-05T04:26:29Z"},{"alias_kind":"pith_short_16","alias_value":"5FH6HBGWGWPB3GRY","created_at":"2026-07-05T04:26:29Z"},{"alias_kind":"pith_short_8","alias_value":"5FH6HBGW","created_at":"2026-07-05T04:26:29Z"}],"graph_snapshots":[{"event_id":"sha256:c44b5df6a2f16cc85f2da6c40c4405a154dc748400c0b4f620a8b79f17c66f06","target":"graph","created_at":"2026-07-05T04:26:29Z","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/2205.12740/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The effectiveness of Object Detection, one of the central problems in computer vision tasks, highly depends on the definition of the loss function - a measure of how accurately your ML model can predict the expected outcome. Conventional object detection loss functions depend on aggregation of metrics of bounding box regression such as the distance, overlap area and aspect ratio of the predicted and ground truth boxes (i.e. GIoU, CIoU, ICIoU etc). However, none of the methods proposed and used to date considers the direction of the mismatch between the desired ground box and the predicted, \"ex","authors_text":"Zhora Gevorgyan","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-05-25T12:46:21Z","title":"SIoU Loss: More Powerful Learning for Bounding Box Regression"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2205.12740","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:dacbe62e1d437d6f00d6fac70b00f16ffcd21ef97d840431b85b3c3dc591bde5","target":"record","created_at":"2026-07-05T04:26:29Z","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":"687ffd4a0473ce9eb571c745c70ad5809576613b2d84c515d9c686e4b4555edc","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-05-25T12:46:21Z","title_canon_sha256":"396b04b5374714e59f52d2756e469f3403d5a6c5847c0563ff33bd337b1deb93"},"schema_version":"1.0","source":{"id":"2205.12740","kind":"arxiv","version":1}},"canonical_sha256":"e94fe384d6359e1d9a3854538114d627fbec81933e7a4d07217ee3b84f82b820","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e94fe384d6359e1d9a3854538114d627fbec81933e7a4d07217ee3b84f82b820","first_computed_at":"2026-07-05T04:26:29.413896Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:26:29.413896Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"EI6/dgTyKBdAOMcxYwBibV42PdICyY3KdozO3V554u839DFbjxsefjpykW093huiHLMT5Ogj+Gy/lbuaGiOdAQ==","signature_status":"signed_v1","signed_at":"2026-07-05T04:26:29.414313Z","signed_message":"canonical_sha256_bytes"},"source_id":"2205.12740","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:dacbe62e1d437d6f00d6fac70b00f16ffcd21ef97d840431b85b3c3dc591bde5","sha256:c44b5df6a2f16cc85f2da6c40c4405a154dc748400c0b4f620a8b79f17c66f06"],"state_sha256":"ed668f7ee4a7f7d34d9f440b173bd24775952855c571822df3453947216ce7a8"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3x6NPdKQ/B2c7kamuwM0+lumcYKjIWjpjADFeU0bBF2IgngQJOLBUOeQ67aRW0vDdpHjx2NFWFAgpeS11J80Bg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-10T02:15:40.520601Z","bundle_sha256":"7082fc8ecab2720404f28f0826525378d3bdd103e1ee82cceae6f88998dbeae3"}}