{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:QDOWIRC3DM4LCVFZVHLAOS6SXZ","short_pith_number":"pith:QDOWIRC3","canonical_record":{"source":{"id":"2109.00895","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2021-08-20T08:01:28Z","cross_cats_sorted":["cs.AI","cs.CL"],"title_canon_sha256":"92b274c732b53ecfc098a4a640dc5df29a50b6e6de4db223c32fe18f9eeb1d99","abstract_canon_sha256":"38dcfc9d872d646c378ab90eed23c3abc5c8f6a470ffd9e001fb67cd8ca19b4c"},"schema_version":"1.0"},"canonical_sha256":"80dd64445b1b38b154b9a9d6074bd2be7f527e7ebcfa06d3ace87cee611d067c","source":{"kind":"arxiv","id":"2109.00895","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2109.00895","created_at":"2026-07-05T03:10:59Z"},{"alias_kind":"arxiv_version","alias_value":"2109.00895v1","created_at":"2026-07-05T03:10:59Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2109.00895","created_at":"2026-07-05T03:10:59Z"},{"alias_kind":"pith_short_12","alias_value":"QDOWIRC3DM4L","created_at":"2026-07-05T03:10:59Z"},{"alias_kind":"pith_short_16","alias_value":"QDOWIRC3DM4LCVFZ","created_at":"2026-07-05T03:10:59Z"},{"alias_kind":"pith_short_8","alias_value":"QDOWIRC3","created_at":"2026-07-05T03:10:59Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:QDOWIRC3DM4LCVFZVHLAOS6SXZ","target":"record","payload":{"canonical_record":{"source":{"id":"2109.00895","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2021-08-20T08:01:28Z","cross_cats_sorted":["cs.AI","cs.CL"],"title_canon_sha256":"92b274c732b53ecfc098a4a640dc5df29a50b6e6de4db223c32fe18f9eeb1d99","abstract_canon_sha256":"38dcfc9d872d646c378ab90eed23c3abc5c8f6a470ffd9e001fb67cd8ca19b4c"},"schema_version":"1.0"},"canonical_sha256":"80dd64445b1b38b154b9a9d6074bd2be7f527e7ebcfa06d3ace87cee611d067c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:10:59.479640Z","signature_b64":"Ze2pn9jp+ya8Q22GZSa6iDy44vzDcBv6L/14GeNSvyGQrSvTSNg741BxP62KXQ4cJ//aL5IvUs0acy85c6a/Bw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"80dd64445b1b38b154b9a9d6074bd2be7f527e7ebcfa06d3ace87cee611d067c","last_reissued_at":"2026-07-05T03:10:59.479230Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:10:59.479230Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2109.00895","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":"2026-07-05T03:10:59Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cNhUzdEta0H/urFxbKTyA4UoG+gfhjpjdJf9uE+77nkG8p8W0YReNq1gC5FX5dQFGAyr8O4Nfnd5+7d15cvBAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T15:46:19.504645Z"},"content_sha256":"fe27ac8c152de45c102d54f19d801b3b652727276a1d3944f212cb1dd35defe3","schema_version":"1.0","event_id":"sha256:fe27ac8c152de45c102d54f19d801b3b652727276a1d3944f212cb1dd35defe3"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:QDOWIRC3DM4LCVFZVHLAOS6SXZ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Knowledge Perceived Multi-modal Pretraining in E-commerce","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CL"],"primary_cat":"cs.CV","authors_text":"Ganqiang Ye, Huaixiao Tou, Huajun Chen, Hui Chen, Ningyu Zhang, Wen Zhang, Yushan Zhu","submitted_at":"2021-08-20T08:01:28Z","abstract_excerpt":"In this paper, we address multi-modal pretraining of product data in the field of E-commerce. Current multi-modal pretraining methods proposed for image and text modalities lack robustness in the face of modality-missing and modality-noise, which are two pervasive problems of multi-modal product data in real E-commerce scenarios. To this end, we propose a novel method, K3M, which introduces knowledge modality in multi-modal pretraining to correct the noise and supplement the missing of image and text modalities. The modal-encoding layer extracts the features of each modality. The modal-interac"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2109.00895","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/2109.00895/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":"2026-07-05T03:10:59Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"hqYpDcweYKvDPwo8NpGCWT/j0PlFVxqLW+2FvwGt+WEX0QUWaKy+Gto/Pt5uKBG8S1e8NG7YnJQH9f4DvA9QBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T15:46:19.505022Z"},"content_sha256":"5e50978252baff7ccbaad671f414b905dd727977a2e3dc911d51b2a1e5e72c62","schema_version":"1.0","event_id":"sha256:5e50978252baff7ccbaad671f414b905dd727977a2e3dc911d51b2a1e5e72c62"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/QDOWIRC3DM4LCVFZVHLAOS6SXZ/bundle.json","state_url":"https://pith.science/pith/QDOWIRC3DM4LCVFZVHLAOS6SXZ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/QDOWIRC3DM4LCVFZVHLAOS6SXZ/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":"2026-07-07T15:46:19Z","links":{"resolver":"https://pith.science/pith/QDOWIRC3DM4LCVFZVHLAOS6SXZ","bundle":"https://pith.science/pith/QDOWIRC3DM4LCVFZVHLAOS6SXZ/bundle.json","state":"https://pith.science/pith/QDOWIRC3DM4LCVFZVHLAOS6SXZ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/QDOWIRC3DM4LCVFZVHLAOS6SXZ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:QDOWIRC3DM4LCVFZVHLAOS6SXZ","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":"38dcfc9d872d646c378ab90eed23c3abc5c8f6a470ffd9e001fb67cd8ca19b4c","cross_cats_sorted":["cs.AI","cs.CL"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2021-08-20T08:01:28Z","title_canon_sha256":"92b274c732b53ecfc098a4a640dc5df29a50b6e6de4db223c32fe18f9eeb1d99"},"schema_version":"1.0","source":{"id":"2109.00895","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2109.00895","created_at":"2026-07-05T03:10:59Z"},{"alias_kind":"arxiv_version","alias_value":"2109.00895v1","created_at":"2026-07-05T03:10:59Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2109.00895","created_at":"2026-07-05T03:10:59Z"},{"alias_kind":"pith_short_12","alias_value":"QDOWIRC3DM4L","created_at":"2026-07-05T03:10:59Z"},{"alias_kind":"pith_short_16","alias_value":"QDOWIRC3DM4LCVFZ","created_at":"2026-07-05T03:10:59Z"},{"alias_kind":"pith_short_8","alias_value":"QDOWIRC3","created_at":"2026-07-05T03:10:59Z"}],"graph_snapshots":[{"event_id":"sha256:5e50978252baff7ccbaad671f414b905dd727977a2e3dc911d51b2a1e5e72c62","target":"graph","created_at":"2026-07-05T03:10:59Z","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/2109.00895/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In this paper, we address multi-modal pretraining of product data in the field of E-commerce. Current multi-modal pretraining methods proposed for image and text modalities lack robustness in the face of modality-missing and modality-noise, which are two pervasive problems of multi-modal product data in real E-commerce scenarios. To this end, we propose a novel method, K3M, which introduces knowledge modality in multi-modal pretraining to correct the noise and supplement the missing of image and text modalities. The modal-encoding layer extracts the features of each modality. The modal-interac","authors_text":"Ganqiang Ye, Huaixiao Tou, Huajun Chen, Hui Chen, Ningyu Zhang, Wen Zhang, Yushan Zhu","cross_cats":["cs.AI","cs.CL"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2021-08-20T08:01:28Z","title":"Knowledge Perceived Multi-modal Pretraining in E-commerce"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2109.00895","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:fe27ac8c152de45c102d54f19d801b3b652727276a1d3944f212cb1dd35defe3","target":"record","created_at":"2026-07-05T03:10:59Z","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":"38dcfc9d872d646c378ab90eed23c3abc5c8f6a470ffd9e001fb67cd8ca19b4c","cross_cats_sorted":["cs.AI","cs.CL"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2021-08-20T08:01:28Z","title_canon_sha256":"92b274c732b53ecfc098a4a640dc5df29a50b6e6de4db223c32fe18f9eeb1d99"},"schema_version":"1.0","source":{"id":"2109.00895","kind":"arxiv","version":1}},"canonical_sha256":"80dd64445b1b38b154b9a9d6074bd2be7f527e7ebcfa06d3ace87cee611d067c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"80dd64445b1b38b154b9a9d6074bd2be7f527e7ebcfa06d3ace87cee611d067c","first_computed_at":"2026-07-05T03:10:59.479230Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:10:59.479230Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Ze2pn9jp+ya8Q22GZSa6iDy44vzDcBv6L/14GeNSvyGQrSvTSNg741BxP62KXQ4cJ//aL5IvUs0acy85c6a/Bw==","signature_status":"signed_v1","signed_at":"2026-07-05T03:10:59.479640Z","signed_message":"canonical_sha256_bytes"},"source_id":"2109.00895","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:fe27ac8c152de45c102d54f19d801b3b652727276a1d3944f212cb1dd35defe3","sha256:5e50978252baff7ccbaad671f414b905dd727977a2e3dc911d51b2a1e5e72c62"],"state_sha256":"ebc00ceb07a9ad2fcc88bd80e339745843d495091e4ee3238a8f139d3e744509"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"kA/jF97eXErCQwoh2aqxrM0f8FXfB1XS2MvO3KwCvRhItdXMElY5P1fdK8fIBuRBDkUmuqVHvGr9ualaA22lBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-07T15:46:19.506982Z","bundle_sha256":"713db2c17028eed2dd774363c173e9cbc57af2ba2b02c63987d68a53a3d6a31a"}}