{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:3EM3OCQWREUVYTPSTCW5IWQGQT","short_pith_number":"pith:3EM3OCQW","canonical_record":{"source":{"id":"2310.19252","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-10-30T03:45:15Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"b70c12eb151bce8a72adad7959d9613bfaf7533e07d98fbca22864f32f26d7b9","abstract_canon_sha256":"6e3a04a3a0c9b25b8fa9b0f0ab81e0c1fddf62f235ae7fccc3bd19db4ff84ad9"},"schema_version":"1.0"},"canonical_sha256":"d919b70a1689295c4df298add45a0684ceb2c7455717a231f822cbc6aedb290a","source":{"kind":"arxiv","id":"2310.19252","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2310.19252","created_at":"2026-07-05T07:06:48Z"},{"alias_kind":"arxiv_version","alias_value":"2310.19252v1","created_at":"2026-07-05T07:06:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.19252","created_at":"2026-07-05T07:06:48Z"},{"alias_kind":"pith_short_12","alias_value":"3EM3OCQWREUV","created_at":"2026-07-05T07:06:48Z"},{"alias_kind":"pith_short_16","alias_value":"3EM3OCQWREUVYTPS","created_at":"2026-07-05T07:06:48Z"},{"alias_kind":"pith_short_8","alias_value":"3EM3OCQW","created_at":"2026-07-05T07:06:48Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:3EM3OCQWREUVYTPSTCW5IWQGQT","target":"record","payload":{"canonical_record":{"source":{"id":"2310.19252","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-10-30T03:45:15Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"b70c12eb151bce8a72adad7959d9613bfaf7533e07d98fbca22864f32f26d7b9","abstract_canon_sha256":"6e3a04a3a0c9b25b8fa9b0f0ab81e0c1fddf62f235ae7fccc3bd19db4ff84ad9"},"schema_version":"1.0"},"canonical_sha256":"d919b70a1689295c4df298add45a0684ceb2c7455717a231f822cbc6aedb290a","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:06:48.360348Z","signature_b64":"e/51PgSILg1gYiwYR5J8M7Iq+HdGnS7/pGgUtZun/4HDI5RVjmi0tqpxl7dBvrhzEAI5KeG62kY4szd5+u+DDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d919b70a1689295c4df298add45a0684ceb2c7455717a231f822cbc6aedb290a","last_reissued_at":"2026-07-05T07:06:48.359850Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:06:48.359850Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2310.19252","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":"2026-07-05T07:06:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ZTiVisf41yfSQI+JcG4JTEcL+6GWjr/nyx6ejgZWJTH/LAq4YWkIZLqlokJyRsYy/qyXTP0SS/jee84Tu9zzBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T00:14:21.063566Z"},"content_sha256":"4318baa9f57af57abeea1e534057e6f4a6b2c64141cc320d61564f9c7ea7f44c","schema_version":"1.0","event_id":"sha256:4318baa9f57af57abeea1e534057e6f4a6b2c64141cc320d61564f9c7ea7f44c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:3EM3OCQWREUVYTPSTCW5IWQGQT","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Revisiting Evaluation Metrics for Semantic Segmentation: Optimization and Evaluation of Fine-grained Intersection over Union","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CV","authors_text":"Amal Rannen-Triki, Devis Tuia, Jiaqian Yu, Luc Van Gool, Matthew B. Blaschko, Maxim Berman, Philip H.S. Torr, Tinne Tuytelaars, Zifu Wang","submitted_at":"2023-10-30T03:45:15Z","abstract_excerpt":"Semantic segmentation datasets often exhibit two types of imbalance: \\textit{class imbalance}, where some classes appear more frequently than others and \\textit{size imbalance}, where some objects occupy more pixels than others. This causes traditional evaluation metrics to be biased towards \\textit{majority classes} (e.g. overall pixel-wise accuracy) and \\textit{large objects} (e.g. mean pixel-wise accuracy and per-dataset mean intersection over union). To address these shortcomings, we propose the use of fine-grained mIoUs along with corresponding worst-case metrics, thereby offering a more "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.19252","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/2310.19252/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":"2026-07-05T07:06:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"tKZtzd49qXGRlp04Sd2xMcb6YgKqiT2d5IMxD1s7YNZcGD+1bXqDMYBCgEGr+N7dkssC19+muelpRVMxKs+UDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T00:14:21.064486Z"},"content_sha256":"b3853b0bab5bfb2fb29f1a9aec0239cde8f43a5655ef58c4cf1d73bbfcf2bbc7","schema_version":"1.0","event_id":"sha256:b3853b0bab5bfb2fb29f1a9aec0239cde8f43a5655ef58c4cf1d73bbfcf2bbc7"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/3EM3OCQWREUVYTPSTCW5IWQGQT/bundle.json","state_url":"https://pith.science/pith/3EM3OCQWREUVYTPSTCW5IWQGQT/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/3EM3OCQWREUVYTPSTCW5IWQGQT/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":"2026-08-07T00:14:21Z","links":{"resolver":"https://pith.science/pith/3EM3OCQWREUVYTPSTCW5IWQGQT","bundle":"https://pith.science/pith/3EM3OCQWREUVYTPSTCW5IWQGQT/bundle.json","state":"https://pith.science/pith/3EM3OCQWREUVYTPSTCW5IWQGQT/state.json","well_known_bundle":"https://pith.science/.well-known/pith/3EM3OCQWREUVYTPSTCW5IWQGQT/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:3EM3OCQWREUVYTPSTCW5IWQGQT","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":"6e3a04a3a0c9b25b8fa9b0f0ab81e0c1fddf62f235ae7fccc3bd19db4ff84ad9","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-10-30T03:45:15Z","title_canon_sha256":"b70c12eb151bce8a72adad7959d9613bfaf7533e07d98fbca22864f32f26d7b9"},"schema_version":"1.0","source":{"id":"2310.19252","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2310.19252","created_at":"2026-07-05T07:06:48Z"},{"alias_kind":"arxiv_version","alias_value":"2310.19252v1","created_at":"2026-07-05T07:06:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.19252","created_at":"2026-07-05T07:06:48Z"},{"alias_kind":"pith_short_12","alias_value":"3EM3OCQWREUV","created_at":"2026-07-05T07:06:48Z"},{"alias_kind":"pith_short_16","alias_value":"3EM3OCQWREUVYTPS","created_at":"2026-07-05T07:06:48Z"},{"alias_kind":"pith_short_8","alias_value":"3EM3OCQW","created_at":"2026-07-05T07:06:48Z"}],"graph_snapshots":[{"event_id":"sha256:b3853b0bab5bfb2fb29f1a9aec0239cde8f43a5655ef58c4cf1d73bbfcf2bbc7","target":"graph","created_at":"2026-07-05T07:06:48Z","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/2310.19252/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Semantic segmentation datasets often exhibit two types of imbalance: \\textit{class imbalance}, where some classes appear more frequently than others and \\textit{size imbalance}, where some objects occupy more pixels than others. This causes traditional evaluation metrics to be biased towards \\textit{majority classes} (e.g. overall pixel-wise accuracy) and \\textit{large objects} (e.g. mean pixel-wise accuracy and per-dataset mean intersection over union). To address these shortcomings, we propose the use of fine-grained mIoUs along with corresponding worst-case metrics, thereby offering a more ","authors_text":"Amal Rannen-Triki, Devis Tuia, Jiaqian Yu, Luc Van Gool, Matthew B. Blaschko, Maxim Berman, Philip H.S. Torr, Tinne Tuytelaars, Zifu Wang","cross_cats":["cs.AI","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-10-30T03:45:15Z","title":"Revisiting Evaluation Metrics for Semantic Segmentation: Optimization and Evaluation of Fine-grained Intersection over Union"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.19252","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":"sha256:4318baa9f57af57abeea1e534057e6f4a6b2c64141cc320d61564f9c7ea7f44c","target":"record","created_at":"2026-07-05T07:06:48Z","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":"6e3a04a3a0c9b25b8fa9b0f0ab81e0c1fddf62f235ae7fccc3bd19db4ff84ad9","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-10-30T03:45:15Z","title_canon_sha256":"b70c12eb151bce8a72adad7959d9613bfaf7533e07d98fbca22864f32f26d7b9"},"schema_version":"1.0","source":{"id":"2310.19252","kind":"arxiv","version":1}},"canonical_sha256":"d919b70a1689295c4df298add45a0684ceb2c7455717a231f822cbc6aedb290a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d919b70a1689295c4df298add45a0684ceb2c7455717a231f822cbc6aedb290a","first_computed_at":"2026-07-05T07:06:48.359850Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:06:48.359850Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"e/51PgSILg1gYiwYR5J8M7Iq+HdGnS7/pGgUtZun/4HDI5RVjmi0tqpxl7dBvrhzEAI5KeG62kY4szd5+u+DDQ==","signature_status":"signed_v1","signed_at":"2026-07-05T07:06:48.360348Z","signed_message":"canonical_sha256_bytes"},"source_id":"2310.19252","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4318baa9f57af57abeea1e534057e6f4a6b2c64141cc320d61564f9c7ea7f44c","sha256:b3853b0bab5bfb2fb29f1a9aec0239cde8f43a5655ef58c4cf1d73bbfcf2bbc7"],"state_sha256":"410af89da7a31219479486d41c2b3c241903ec46287277a5cf213ab3adf77c0e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"SRNlSU43dFzq3mbSRHSnNgsXQ2X3jiXgalymE5YUXYxF9xuVYKigjMcfMKiw2IakBxT4LAjT3VRHGELHx+RmDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-07T00:14:21.070523Z","bundle_sha256":"9c39ce3c06e14bfd173608207f558c4405477d4b5c9c1abfb5fed9769c235b99"}}