{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:2OGESQPRLK33J2AUOCIW23SICE","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":"13160052661d6d8ea6dcd015ef2525262ada78435e86e8aaa5501b01cb82b9ba","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-09-20T13:53:13Z","title_canon_sha256":"78afd37882c353bdceaae87d66c46f6d75cef8f41c94b2dcb83e12b7ef70f091"},"schema_version":"1.0","source":{"id":"2409.13513","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2409.13513","created_at":"2026-07-05T09:09:36Z"},{"alias_kind":"arxiv_version","alias_value":"2409.13513v1","created_at":"2026-07-05T09:09:36Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.13513","created_at":"2026-07-05T09:09:36Z"},{"alias_kind":"pith_short_12","alias_value":"2OGESQPRLK33","created_at":"2026-07-05T09:09:36Z"},{"alias_kind":"pith_short_16","alias_value":"2OGESQPRLK33J2AU","created_at":"2026-07-05T09:09:36Z"},{"alias_kind":"pith_short_8","alias_value":"2OGESQPR","created_at":"2026-07-05T09:09:36Z"}],"graph_snapshots":[{"event_id":"sha256:4b37d1ca9f13e908b7e8bc3e6ecb521a3b36bddf342ef556180bebca1d6e6991","target":"graph","created_at":"2026-07-05T09:09:36Z","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/2409.13513/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Current image retrieval systems often face domain specificity and generalization issues. This study aims to overcome these limitations by developing a computationally efficient training framework for a universal feature extractor that provides strong semantic image representations across various domains. To this end, we curated a multi-domain training dataset, called M4D-35k, which allows for resource-efficient training. Additionally, we conduct an extensive evaluation and comparison of various state-of-the-art visual-semantic foundation models and margin-based metric learning loss functions r","authors_text":"Bj\\\"orn Barz, David Tschirschwitz, Morris Florek, Volker Rodehorst","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-09-20T13:53:13Z","title":"Efficient and Discriminative Image Feature Extraction for Universal Image Retrieval"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.13513","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":"sha256:47bfc6177b2dbeac73988e5230862715143c4c477b03c61508b51734d5d11148","target":"record","created_at":"2026-07-05T09:09:36Z","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":"13160052661d6d8ea6dcd015ef2525262ada78435e86e8aaa5501b01cb82b9ba","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-09-20T13:53:13Z","title_canon_sha256":"78afd37882c353bdceaae87d66c46f6d75cef8f41c94b2dcb83e12b7ef70f091"},"schema_version":"1.0","source":{"id":"2409.13513","kind":"arxiv","version":1}},"canonical_sha256":"d38c4941f15ab7b4e81470916d6e48111570aa7af200319f59ca388d8cac4281","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d38c4941f15ab7b4e81470916d6e48111570aa7af200319f59ca388d8cac4281","first_computed_at":"2026-07-05T09:09:36.739424Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:09:36.739424Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"QpHIAwwcGAOzhzDvDiPeibVRVztJZie5+fyCjQH2gtvfTmHgQGMVn/TQ5Uft0a+XvMKGkEy7/FZUQ9fYg/A+CQ==","signature_status":"signed_v1","signed_at":"2026-07-05T09:09:36.739950Z","signed_message":"canonical_sha256_bytes"},"source_id":"2409.13513","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:47bfc6177b2dbeac73988e5230862715143c4c477b03c61508b51734d5d11148","sha256:4b37d1ca9f13e908b7e8bc3e6ecb521a3b36bddf342ef556180bebca1d6e6991"],"state_sha256":"2c67132554cb19df71c52cca69fc8c37ef906544e76fac68f1691a41d9740bdd"}