{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:U3PS432VIYMMVAQBYU76LBVN6G","short_pith_number":"pith:U3PS432V","canonical_record":{"source":{"id":"2011.09157","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2020-11-18T08:42:32Z","cross_cats_sorted":[],"title_canon_sha256":"4aa6129a5ea7efd59de985a0994fa73737c3d3e219c3714fdc0fef1e5b39ee66","abstract_canon_sha256":"5f37d630a7c5f32025de8073e1153c978ccf922e5b2266c0d3a3edeeac01bc58"},"schema_version":"1.0"},"canonical_sha256":"a6df2e6f554618ca8201c53fe586adf18143ccf198708fabb562c52b12e8b4f5","source":{"kind":"arxiv","id":"2011.09157","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2011.09157","created_at":"2026-07-05T02:28:53Z"},{"alias_kind":"arxiv_version","alias_value":"2011.09157v2","created_at":"2026-07-05T02:28:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2011.09157","created_at":"2026-07-05T02:28:53Z"},{"alias_kind":"pith_short_12","alias_value":"U3PS432VIYMM","created_at":"2026-07-05T02:28:53Z"},{"alias_kind":"pith_short_16","alias_value":"U3PS432VIYMMVAQB","created_at":"2026-07-05T02:28:53Z"},{"alias_kind":"pith_short_8","alias_value":"U3PS432V","created_at":"2026-07-05T02:28:53Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:U3PS432VIYMMVAQBYU76LBVN6G","target":"record","payload":{"canonical_record":{"source":{"id":"2011.09157","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2020-11-18T08:42:32Z","cross_cats_sorted":[],"title_canon_sha256":"4aa6129a5ea7efd59de985a0994fa73737c3d3e219c3714fdc0fef1e5b39ee66","abstract_canon_sha256":"5f37d630a7c5f32025de8073e1153c978ccf922e5b2266c0d3a3edeeac01bc58"},"schema_version":"1.0"},"canonical_sha256":"a6df2e6f554618ca8201c53fe586adf18143ccf198708fabb562c52b12e8b4f5","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:28:53.656367Z","signature_b64":"7Xni39RWHW4Bkh4+tCurBQ+6ctSHrorkROSFQvwivIy9uXzGfwEW3ExXZHj47wn2yJRKxfv0MamvOZaHRvVOAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a6df2e6f554618ca8201c53fe586adf18143ccf198708fabb562c52b12e8b4f5","last_reissued_at":"2026-07-05T02:28:53.655865Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:28:53.655865Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2011.09157","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":"2026-07-05T02:28:53Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"RGlpsXntECecFuORRTwVnu4XiwL1YaMdviJsih4tqCz9Wocf8thL1/Mk1tUlUZAa1wx9b4B5RoE3+dI2KFLyCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T16:05:39.609760Z"},"content_sha256":"ae688290e2084fdd1733ea772352b617814c0925362d33aa3f71b33542db735b","schema_version":"1.0","event_id":"sha256:ae688290e2084fdd1733ea772352b617814c0925362d33aa3f71b33542db735b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:U3PS432VIYMMVAQBYU76LBVN6G","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Dense Contrastive Learning for Self-Supervised Visual Pre-Training","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chunhua Shen, Lei Li, Rufeng Zhang, Tao Kong, Xinlong Wang","submitted_at":"2020-11-18T08:42:32Z","abstract_excerpt":"To date, most existing self-supervised learning methods are designed and optimized for image classification. These pre-trained models can be sub-optimal for dense prediction tasks due to the discrepancy between image-level prediction and pixel-level prediction. To fill this gap, we aim to design an effective, dense self-supervised learning method that directly works at the level of pixels (or local features) by taking into account the correspondence between local features. We present dense contrastive learning, which implements self-supervised learning by optimizing a pairwise contrastive (dis"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2011.09157","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/2011.09157/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":"2026-07-05T02:28:53Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"QdHoDSv0V7VkdWkPiwwZSq9nRFUkeQAbjGBatl9vmo07yyImb+Pj6LRx5tt0Z1rleGiG2VPxxuGxklpBCwOHAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T16:05:39.610177Z"},"content_sha256":"e75845838728d9d7e3b4cf5baf2eb5059bac842a02accddfccca6db2fe2dae6e","schema_version":"1.0","event_id":"sha256:e75845838728d9d7e3b4cf5baf2eb5059bac842a02accddfccca6db2fe2dae6e"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/U3PS432VIYMMVAQBYU76LBVN6G/bundle.json","state_url":"https://pith.science/pith/U3PS432VIYMMVAQBYU76LBVN6G/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/U3PS432VIYMMVAQBYU76LBVN6G/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":"2026-08-06T16:05:39Z","links":{"resolver":"https://pith.science/pith/U3PS432VIYMMVAQBYU76LBVN6G","bundle":"https://pith.science/pith/U3PS432VIYMMVAQBYU76LBVN6G/bundle.json","state":"https://pith.science/pith/U3PS432VIYMMVAQBYU76LBVN6G/state.json","well_known_bundle":"https://pith.science/.well-known/pith/U3PS432VIYMMVAQBYU76LBVN6G/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:U3PS432VIYMMVAQBYU76LBVN6G","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":"5f37d630a7c5f32025de8073e1153c978ccf922e5b2266c0d3a3edeeac01bc58","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2020-11-18T08:42:32Z","title_canon_sha256":"4aa6129a5ea7efd59de985a0994fa73737c3d3e219c3714fdc0fef1e5b39ee66"},"schema_version":"1.0","source":{"id":"2011.09157","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2011.09157","created_at":"2026-07-05T02:28:53Z"},{"alias_kind":"arxiv_version","alias_value":"2011.09157v2","created_at":"2026-07-05T02:28:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2011.09157","created_at":"2026-07-05T02:28:53Z"},{"alias_kind":"pith_short_12","alias_value":"U3PS432VIYMM","created_at":"2026-07-05T02:28:53Z"},{"alias_kind":"pith_short_16","alias_value":"U3PS432VIYMMVAQB","created_at":"2026-07-05T02:28:53Z"},{"alias_kind":"pith_short_8","alias_value":"U3PS432V","created_at":"2026-07-05T02:28:53Z"}],"graph_snapshots":[{"event_id":"sha256:e75845838728d9d7e3b4cf5baf2eb5059bac842a02accddfccca6db2fe2dae6e","target":"graph","created_at":"2026-07-05T02:28:53Z","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/2011.09157/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"To date, most existing self-supervised learning methods are designed and optimized for image classification. These pre-trained models can be sub-optimal for dense prediction tasks due to the discrepancy between image-level prediction and pixel-level prediction. To fill this gap, we aim to design an effective, dense self-supervised learning method that directly works at the level of pixels (or local features) by taking into account the correspondence between local features. We present dense contrastive learning, which implements self-supervised learning by optimizing a pairwise contrastive (dis","authors_text":"Chunhua Shen, Lei Li, Rufeng Zhang, Tao Kong, Xinlong Wang","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2020-11-18T08:42:32Z","title":"Dense Contrastive Learning for Self-Supervised Visual Pre-Training"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2011.09157","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:ae688290e2084fdd1733ea772352b617814c0925362d33aa3f71b33542db735b","target":"record","created_at":"2026-07-05T02:28:53Z","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":"5f37d630a7c5f32025de8073e1153c978ccf922e5b2266c0d3a3edeeac01bc58","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2020-11-18T08:42:32Z","title_canon_sha256":"4aa6129a5ea7efd59de985a0994fa73737c3d3e219c3714fdc0fef1e5b39ee66"},"schema_version":"1.0","source":{"id":"2011.09157","kind":"arxiv","version":2}},"canonical_sha256":"a6df2e6f554618ca8201c53fe586adf18143ccf198708fabb562c52b12e8b4f5","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a6df2e6f554618ca8201c53fe586adf18143ccf198708fabb562c52b12e8b4f5","first_computed_at":"2026-07-05T02:28:53.655865Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:28:53.655865Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"7Xni39RWHW4Bkh4+tCurBQ+6ctSHrorkROSFQvwivIy9uXzGfwEW3ExXZHj47wn2yJRKxfv0MamvOZaHRvVOAw==","signature_status":"signed_v1","signed_at":"2026-07-05T02:28:53.656367Z","signed_message":"canonical_sha256_bytes"},"source_id":"2011.09157","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ae688290e2084fdd1733ea772352b617814c0925362d33aa3f71b33542db735b","sha256:e75845838728d9d7e3b4cf5baf2eb5059bac842a02accddfccca6db2fe2dae6e"],"state_sha256":"179dec86fe364a63ce2e76396406099290deee0b2a5f0a5bf99f199233960b37"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"TnefyutLn8zvsb526UA24F9oCAs5wCGsb9GSBbJvDjKfoNQ4RRAFLPA50C/54RzkGnWvQ35R7Un0f4wvOwRQBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-06T16:05:39.613198Z","bundle_sha256":"07ebe7b3c6999a6ebd3a1ca45cd8103b1b8089d103605489e703688bb58cad58"}}