{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:Q3ZQHCI4KFNXF2DIOZDDVOPPKT","short_pith_number":"pith:Q3ZQHCI4","canonical_record":{"source":{"id":"2202.07247","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.CV","submitted_at":"2022-02-15T08:23:59Z","cross_cats_sorted":["cs.AI","cs.CL","cs.MM","cs.SI"],"title_canon_sha256":"7fe858723fe735738df462d3dfff6164e950461d63846ff7a49a4b29d2f14854","abstract_canon_sha256":"eae711a902e73341738a6ac9eb06708e8a432df3162108743e47e9437ce01a76"},"schema_version":"1.0"},"canonical_sha256":"86f303891c515b72e86876463ab9ef54f9b4835b357ce2eba6c49aa05673389d","source":{"kind":"arxiv","id":"2202.07247","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2202.07247","created_at":"2026-07-05T03:56:58Z"},{"alias_kind":"arxiv_version","alias_value":"2202.07247v1","created_at":"2026-07-05T03:56:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2202.07247","created_at":"2026-07-05T03:56:58Z"},{"alias_kind":"pith_short_12","alias_value":"Q3ZQHCI4KFNX","created_at":"2026-07-05T03:56:58Z"},{"alias_kind":"pith_short_16","alias_value":"Q3ZQHCI4KFNXF2DI","created_at":"2026-07-05T03:56:58Z"},{"alias_kind":"pith_short_8","alias_value":"Q3ZQHCI4","created_at":"2026-07-05T03:56:58Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:Q3ZQHCI4KFNXF2DIOZDDVOPPKT","target":"record","payload":{"canonical_record":{"source":{"id":"2202.07247","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.CV","submitted_at":"2022-02-15T08:23:59Z","cross_cats_sorted":["cs.AI","cs.CL","cs.MM","cs.SI"],"title_canon_sha256":"7fe858723fe735738df462d3dfff6164e950461d63846ff7a49a4b29d2f14854","abstract_canon_sha256":"eae711a902e73341738a6ac9eb06708e8a432df3162108743e47e9437ce01a76"},"schema_version":"1.0"},"canonical_sha256":"86f303891c515b72e86876463ab9ef54f9b4835b357ce2eba6c49aa05673389d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:56:58.112651Z","signature_b64":"LC3MaOUAAqZop6XkBDrqK7Np9WSyW5EpNsAEiaF9fzhe4zbQJEDybx1OOZZ5Ukd3IOfy3EpWNWjyTY+zNmTnCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"86f303891c515b72e86876463ab9ef54f9b4835b357ce2eba6c49aa05673389d","last_reissued_at":"2026-07-05T03:56:58.112200Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:56:58.112200Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2202.07247","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":"2026-07-05T03:56:58Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"FxNxmM8vGTiNxzDTOjV3AF+CKlCPkyJS13HQpeiAQxcxHJ3j4NeN27VgU+Mh57JgT5qe6Y7f5SUf2Wqqlv+9Aw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T09:18:57.557309Z"},"content_sha256":"818896090e137c82434185f5646d1574485e89418852061aac048df1b34146fc","schema_version":"1.0","event_id":"sha256:818896090e137c82434185f5646d1574485e89418852061aac048df1b34146fc"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:Q3ZQHCI4KFNXF2DIOZDDVOPPKT","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"CommerceMM: Large-Scale Commerce MultiModal Representation Learning with Omni Retrieval","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"","cross_cats":["cs.AI","cs.CL","cs.MM","cs.SI"],"primary_cat":"cs.CV","authors_text":"Animesh Sinha, Hugo Chen, Jun Chen, Licheng Yu, Mengjiao MJ Wang, Ning Zhang, Tamara L. Berg","submitted_at":"2022-02-15T08:23:59Z","abstract_excerpt":"We introduce CommerceMM - a multimodal model capable of providing a diverse and granular understanding of commerce topics associated to the given piece of content (image, text, image+text), and having the capability to generalize to a wide range of tasks, including Multimodal Categorization, Image-Text Retrieval, Query-to-Product Retrieval, Image-to-Product Retrieval, etc. We follow the pre-training + fine-tuning training regime and present 5 effective pre-training tasks on image-text pairs. To embrace more common and diverse commerce data with text-to-multimodal, image-to-multimodal, and mult"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2202.07247","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/2202.07247/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":"2026-07-05T03:56:58Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"rOXwIUnHqZSZhhVyvkUqqT3wyWV3abcRvCyyxlUNrA+Rc2oi9GmmnDxMfdqW2X34fNsWWbb2pC8TvX7x1L3XDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T09:18:57.557701Z"},"content_sha256":"310008e5ecf4df39f605cdfc812a3587e86adccd36bc13bece98daaf29fb7e79","schema_version":"1.0","event_id":"sha256:310008e5ecf4df39f605cdfc812a3587e86adccd36bc13bece98daaf29fb7e79"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/Q3ZQHCI4KFNXF2DIOZDDVOPPKT/bundle.json","state_url":"https://pith.science/pith/Q3ZQHCI4KFNXF2DIOZDDVOPPKT/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/Q3ZQHCI4KFNXF2DIOZDDVOPPKT/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":"2026-07-07T09:18:57Z","links":{"resolver":"https://pith.science/pith/Q3ZQHCI4KFNXF2DIOZDDVOPPKT","bundle":"https://pith.science/pith/Q3ZQHCI4KFNXF2DIOZDDVOPPKT/bundle.json","state":"https://pith.science/pith/Q3ZQHCI4KFNXF2DIOZDDVOPPKT/state.json","well_known_bundle":"https://pith.science/.well-known/pith/Q3ZQHCI4KFNXF2DIOZDDVOPPKT/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:Q3ZQHCI4KFNXF2DIOZDDVOPPKT","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":"eae711a902e73341738a6ac9eb06708e8a432df3162108743e47e9437ce01a76","cross_cats_sorted":["cs.AI","cs.CL","cs.MM","cs.SI"],"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.CV","submitted_at":"2022-02-15T08:23:59Z","title_canon_sha256":"7fe858723fe735738df462d3dfff6164e950461d63846ff7a49a4b29d2f14854"},"schema_version":"1.0","source":{"id":"2202.07247","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2202.07247","created_at":"2026-07-05T03:56:58Z"},{"alias_kind":"arxiv_version","alias_value":"2202.07247v1","created_at":"2026-07-05T03:56:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2202.07247","created_at":"2026-07-05T03:56:58Z"},{"alias_kind":"pith_short_12","alias_value":"Q3ZQHCI4KFNX","created_at":"2026-07-05T03:56:58Z"},{"alias_kind":"pith_short_16","alias_value":"Q3ZQHCI4KFNXF2DI","created_at":"2026-07-05T03:56:58Z"},{"alias_kind":"pith_short_8","alias_value":"Q3ZQHCI4","created_at":"2026-07-05T03:56:58Z"}],"graph_snapshots":[{"event_id":"sha256:310008e5ecf4df39f605cdfc812a3587e86adccd36bc13bece98daaf29fb7e79","target":"graph","created_at":"2026-07-05T03:56:58Z","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/2202.07247/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We introduce CommerceMM - a multimodal model capable of providing a diverse and granular understanding of commerce topics associated to the given piece of content (image, text, image+text), and having the capability to generalize to a wide range of tasks, including Multimodal Categorization, Image-Text Retrieval, Query-to-Product Retrieval, Image-to-Product Retrieval, etc. We follow the pre-training + fine-tuning training regime and present 5 effective pre-training tasks on image-text pairs. To embrace more common and diverse commerce data with text-to-multimodal, image-to-multimodal, and mult","authors_text":"Animesh Sinha, Hugo Chen, Jun Chen, Licheng Yu, Mengjiao MJ Wang, Ning Zhang, Tamara L. Berg","cross_cats":["cs.AI","cs.CL","cs.MM","cs.SI"],"headline":"","license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.CV","submitted_at":"2022-02-15T08:23:59Z","title":"CommerceMM: Large-Scale Commerce MultiModal Representation Learning with Omni Retrieval"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2202.07247","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":"sha256:818896090e137c82434185f5646d1574485e89418852061aac048df1b34146fc","target":"record","created_at":"2026-07-05T03:56:58Z","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":"eae711a902e73341738a6ac9eb06708e8a432df3162108743e47e9437ce01a76","cross_cats_sorted":["cs.AI","cs.CL","cs.MM","cs.SI"],"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.CV","submitted_at":"2022-02-15T08:23:59Z","title_canon_sha256":"7fe858723fe735738df462d3dfff6164e950461d63846ff7a49a4b29d2f14854"},"schema_version":"1.0","source":{"id":"2202.07247","kind":"arxiv","version":1}},"canonical_sha256":"86f303891c515b72e86876463ab9ef54f9b4835b357ce2eba6c49aa05673389d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"86f303891c515b72e86876463ab9ef54f9b4835b357ce2eba6c49aa05673389d","first_computed_at":"2026-07-05T03:56:58.112200Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:56:58.112200Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"LC3MaOUAAqZop6XkBDrqK7Np9WSyW5EpNsAEiaF9fzhe4zbQJEDybx1OOZZ5Ukd3IOfy3EpWNWjyTY+zNmTnCA==","signature_status":"signed_v1","signed_at":"2026-07-05T03:56:58.112651Z","signed_message":"canonical_sha256_bytes"},"source_id":"2202.07247","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:818896090e137c82434185f5646d1574485e89418852061aac048df1b34146fc","sha256:310008e5ecf4df39f605cdfc812a3587e86adccd36bc13bece98daaf29fb7e79"],"state_sha256":"cfe37975ddc767cf419248bbc917def2d13fb6a9d8fcb2a3a8cf9836fd0f3f9b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"1K5Matu8jBMS3Heu3h9qSwSRN8xKdrkMHFixu88+Cj0QStf3tOt3NuMPRX2fAiGDiLJWN6Vi3kNJQzeAoa6+AA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-07T09:18:57.559670Z","bundle_sha256":"505e0c9a9693b5d3878419ce139513ffe97c80fcc9d57fcf842caf0d3ceb205b"}}