{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:CJBJCSSSEBMCUVBWMWUMTPF76O","short_pith_number":"pith:CJBJCSSS","canonical_record":{"source":{"id":"2309.16889","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-09-28T23:09:30Z","cross_cats_sorted":[],"title_canon_sha256":"5f0b10ceac09f215cf0bed104121ea3b4487ed0cef8e0a6aa604ecfa1c61da1e","abstract_canon_sha256":"bb543fe95d8d7646da4cd11cba6c91a88e088b0e3129a065cd5b26c11827a233"},"schema_version":"1.0"},"canonical_sha256":"1242914a5220582a543665a8c9bcbff392f18dcf66f57b904eb6f0885e0fd6ec","source":{"kind":"arxiv","id":"2309.16889","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2309.16889","created_at":"2026-07-05T06:56:39Z"},{"alias_kind":"arxiv_version","alias_value":"2309.16889v2","created_at":"2026-07-05T06:56:39Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2309.16889","created_at":"2026-07-05T06:56:39Z"},{"alias_kind":"pith_short_12","alias_value":"CJBJCSSSEBMC","created_at":"2026-07-05T06:56:39Z"},{"alias_kind":"pith_short_16","alias_value":"CJBJCSSSEBMCUVBW","created_at":"2026-07-05T06:56:39Z"},{"alias_kind":"pith_short_8","alias_value":"CJBJCSSS","created_at":"2026-07-05T06:56:39Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:CJBJCSSSEBMCUVBWMWUMTPF76O","target":"record","payload":{"canonical_record":{"source":{"id":"2309.16889","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-09-28T23:09:30Z","cross_cats_sorted":[],"title_canon_sha256":"5f0b10ceac09f215cf0bed104121ea3b4487ed0cef8e0a6aa604ecfa1c61da1e","abstract_canon_sha256":"bb543fe95d8d7646da4cd11cba6c91a88e088b0e3129a065cd5b26c11827a233"},"schema_version":"1.0"},"canonical_sha256":"1242914a5220582a543665a8c9bcbff392f18dcf66f57b904eb6f0885e0fd6ec","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:56:39.359869Z","signature_b64":"x/j20RQutfpCofkfz5I9K3LrPgClnpaulnzxir2/yhnVNMdQjGu1AFoP6v3SB2AiArcCDC3X5lkLyYPyxs0WBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1242914a5220582a543665a8c9bcbff392f18dcf66f57b904eb6f0885e0fd6ec","last_reissued_at":"2026-07-05T06:56:39.359452Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:56:39.359452Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2309.16889","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T06:56:39Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"5etGeGqsnBVTFff88ZjuTBnFy/cbvo35JuVOvmbf7Cm/7BlmtpLVzNafY1tU/U2WDHKatTNUAuEzE/CKueiqBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-08T16:45:10.416400Z"},"content_sha256":"de8f6fdb4513694096e5237ce649a8dcab2d45c4e6062fdd2091586d5a55db68","schema_version":"1.0","event_id":"sha256:de8f6fdb4513694096e5237ce649a8dcab2d45c4e6062fdd2091586d5a55db68"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:CJBJCSSSEBMCUVBWMWUMTPF76O","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Superpixel Transformers for Efficient Semantic Segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Alex Zihao Zhu, Hang Yan, Henrik Kretzschmar, Jieru Mei, Liang-Chieh Chen, Siyuan Qiao, Yukun Zhu","submitted_at":"2023-09-28T23:09:30Z","abstract_excerpt":"Semantic segmentation, which aims to classify every pixel in an image, is a key task in machine perception, with many applications across robotics and autonomous driving. Due to the high dimensionality of this task, most existing approaches use local operations, such as convolutions, to generate per-pixel features. However, these methods are typically unable to effectively leverage global context information due to the high computational costs of operating on a dense image. In this work, we propose a solution to this issue by leveraging the idea of superpixels, an over-segmentation of the imag"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2309.16889","kind":"arxiv","version":2},"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/2309.16889/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":"2026-07-05T06:56:39Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8rg9PM/lJlXNhfqApVgXJJRrsHhn7wSJtMv+KMzjwsFJAjTr4sRvSnKIIfk9wipdQSsJJBOMrOwi98WuOSuRDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-08T16:45:10.416778Z"},"content_sha256":"3f18deb2e352757ffbeb2198f5421309e00ab86ef1ae3e48d411d6b6cc621afe","schema_version":"1.0","event_id":"sha256:3f18deb2e352757ffbeb2198f5421309e00ab86ef1ae3e48d411d6b6cc621afe"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/CJBJCSSSEBMCUVBWMWUMTPF76O/bundle.json","state_url":"https://pith.science/pith/CJBJCSSSEBMCUVBWMWUMTPF76O/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/CJBJCSSSEBMCUVBWMWUMTPF76O/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":"2026-07-08T16:45:10Z","links":{"resolver":"https://pith.science/pith/CJBJCSSSEBMCUVBWMWUMTPF76O","bundle":"https://pith.science/pith/CJBJCSSSEBMCUVBWMWUMTPF76O/bundle.json","state":"https://pith.science/pith/CJBJCSSSEBMCUVBWMWUMTPF76O/state.json","well_known_bundle":"https://pith.science/.well-known/pith/CJBJCSSSEBMCUVBWMWUMTPF76O/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:CJBJCSSSEBMCUVBWMWUMTPF76O","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":"bb543fe95d8d7646da4cd11cba6c91a88e088b0e3129a065cd5b26c11827a233","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-09-28T23:09:30Z","title_canon_sha256":"5f0b10ceac09f215cf0bed104121ea3b4487ed0cef8e0a6aa604ecfa1c61da1e"},"schema_version":"1.0","source":{"id":"2309.16889","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2309.16889","created_at":"2026-07-05T06:56:39Z"},{"alias_kind":"arxiv_version","alias_value":"2309.16889v2","created_at":"2026-07-05T06:56:39Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2309.16889","created_at":"2026-07-05T06:56:39Z"},{"alias_kind":"pith_short_12","alias_value":"CJBJCSSSEBMC","created_at":"2026-07-05T06:56:39Z"},{"alias_kind":"pith_short_16","alias_value":"CJBJCSSSEBMCUVBW","created_at":"2026-07-05T06:56:39Z"},{"alias_kind":"pith_short_8","alias_value":"CJBJCSSS","created_at":"2026-07-05T06:56:39Z"}],"graph_snapshots":[{"event_id":"sha256:3f18deb2e352757ffbeb2198f5421309e00ab86ef1ae3e48d411d6b6cc621afe","target":"graph","created_at":"2026-07-05T06:56:39Z","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/2309.16889/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Semantic segmentation, which aims to classify every pixel in an image, is a key task in machine perception, with many applications across robotics and autonomous driving. Due to the high dimensionality of this task, most existing approaches use local operations, such as convolutions, to generate per-pixel features. However, these methods are typically unable to effectively leverage global context information due to the high computational costs of operating on a dense image. In this work, we propose a solution to this issue by leveraging the idea of superpixels, an over-segmentation of the imag","authors_text":"Alex Zihao Zhu, Hang Yan, Henrik Kretzschmar, Jieru Mei, Liang-Chieh Chen, Siyuan Qiao, Yukun Zhu","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-09-28T23:09:30Z","title":"Superpixel Transformers for Efficient Semantic Segmentation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2309.16889","kind":"arxiv","version":2},"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:de8f6fdb4513694096e5237ce649a8dcab2d45c4e6062fdd2091586d5a55db68","target":"record","created_at":"2026-07-05T06:56:39Z","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":"bb543fe95d8d7646da4cd11cba6c91a88e088b0e3129a065cd5b26c11827a233","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-09-28T23:09:30Z","title_canon_sha256":"5f0b10ceac09f215cf0bed104121ea3b4487ed0cef8e0a6aa604ecfa1c61da1e"},"schema_version":"1.0","source":{"id":"2309.16889","kind":"arxiv","version":2}},"canonical_sha256":"1242914a5220582a543665a8c9bcbff392f18dcf66f57b904eb6f0885e0fd6ec","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1242914a5220582a543665a8c9bcbff392f18dcf66f57b904eb6f0885e0fd6ec","first_computed_at":"2026-07-05T06:56:39.359452Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:56:39.359452Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"x/j20RQutfpCofkfz5I9K3LrPgClnpaulnzxir2/yhnVNMdQjGu1AFoP6v3SB2AiArcCDC3X5lkLyYPyxs0WBg==","signature_status":"signed_v1","signed_at":"2026-07-05T06:56:39.359869Z","signed_message":"canonical_sha256_bytes"},"source_id":"2309.16889","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:de8f6fdb4513694096e5237ce649a8dcab2d45c4e6062fdd2091586d5a55db68","sha256:3f18deb2e352757ffbeb2198f5421309e00ab86ef1ae3e48d411d6b6cc621afe"],"state_sha256":"bdd592096ed38bcec9e2a0dddf183ebd042bccee41f335f31556bd2470dcbd0f"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"jd05ACMI4hpaK/9tc2+2x968Y4GXiMUcQYxVGRxszut9T+TpnXE6StZ98ggMDjNTcQ8UT6f7ZE8i5Rph02NtAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-08T16:45:10.418917Z","bundle_sha256":"2a45d39defd3dd37457e5573044047f25013dda9cf04e37cffbdf3290dae728c"}}