{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:7E4LOX5JT7XUWQREOZRVRYQJXZ","short_pith_number":"pith:7E4LOX5J","schema_version":"1.0","canonical_sha256":"f938b75fa99fef4b4224766358e209be53df24e5f8201e6f3f4413cbabc5b9ab","source":{"kind":"arxiv","id":"2407.07315","version":2},"attestation_state":"computed","paper":{"title":"CosmoCLIP: Generalizing Large Vision-Language Models for Astronomical Imaging","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Fakhri Karray, Mohammed Talha Alam, Mohsen Guizani, Raza Imam, Umaima Rahman","submitted_at":"2024-07-10T02:24:43Z","abstract_excerpt":"Existing vision-text contrastive learning models enhance representation transferability and support zero-shot prediction by matching paired image and caption embeddings while pushing unrelated pairs apart. However, astronomical image-label datasets are significantly smaller compared to general image and label datasets available from the internet. We introduce CosmoCLIP, an astronomical image-text contrastive learning framework precisely fine-tuned on the pre-trained CLIP model using SpaceNet and BLIP-based captions. SpaceNet, attained via FLARE, constitutes ~13k optimally distributed images, w"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2407.07315","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-07-10T02:24:43Z","cross_cats_sorted":[],"title_canon_sha256":"f68cef50e61a9cbc58515abc4628c962e6467b4584d2eee1294da0a0ee965a6f","abstract_canon_sha256":"38a18823fe59f071f3b075560b6623597cc1720e9135855dedd6eb239ba22f71"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:38:27.878019Z","signature_b64":"/ikbG/i2ZR78CUk/fw3FXeKDvhgTCZBH8ChDDUXKCsR3SmcIIX/A+I382DP/G/gtWD4Aw/hx/VLrQ2wRPaHGCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f938b75fa99fef4b4224766358e209be53df24e5f8201e6f3f4413cbabc5b9ab","last_reissued_at":"2026-07-05T09:38:27.877521Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:38:27.877521Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"CosmoCLIP: Generalizing Large Vision-Language Models for Astronomical Imaging","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Fakhri Karray, Mohammed Talha Alam, Mohsen Guizani, Raza Imam, Umaima Rahman","submitted_at":"2024-07-10T02:24:43Z","abstract_excerpt":"Existing vision-text contrastive learning models enhance representation transferability and support zero-shot prediction by matching paired image and caption embeddings while pushing unrelated pairs apart. However, astronomical image-label datasets are significantly smaller compared to general image and label datasets available from the internet. We introduce CosmoCLIP, an astronomical image-text contrastive learning framework precisely fine-tuned on the pre-trained CLIP model using SpaceNet and BLIP-based captions. SpaceNet, attained via FLARE, constitutes ~13k optimally distributed images, w"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.07315","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/2407.07315/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2407.07315","created_at":"2026-07-05T09:38:27.877577+00:00"},{"alias_kind":"arxiv_version","alias_value":"2407.07315v2","created_at":"2026-07-05T09:38:27.877577+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.07315","created_at":"2026-07-05T09:38:27.877577+00:00"},{"alias_kind":"pith_short_12","alias_value":"7E4LOX5JT7XU","created_at":"2026-07-05T09:38:27.877577+00:00"},{"alias_kind":"pith_short_16","alias_value":"7E4LOX5JT7XUWQRE","created_at":"2026-07-05T09:38:27.877577+00:00"},{"alias_kind":"pith_short_8","alias_value":"7E4LOX5J","created_at":"2026-07-05T09:38:27.877577+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2607.06305","citing_title":"Exploring Image-Text Alignment for Radio Galaxy Morphologies","ref_index":10,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/7E4LOX5JT7XUWQREOZRVRYQJXZ","json":"https://pith.science/pith/7E4LOX5JT7XUWQREOZRVRYQJXZ.json","graph_json":"https://pith.science/api/pith-number/7E4LOX5JT7XUWQREOZRVRYQJXZ/graph.json","events_json":"https://pith.science/api/pith-number/7E4LOX5JT7XUWQREOZRVRYQJXZ/events.json","paper":"https://pith.science/paper/7E4LOX5J"},"agent_actions":{"view_html":"https://pith.science/pith/7E4LOX5JT7XUWQREOZRVRYQJXZ","download_json":"https://pith.science/pith/7E4LOX5JT7XUWQREOZRVRYQJXZ.json","view_paper":"https://pith.science/paper/7E4LOX5J","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2407.07315&json=true","fetch_graph":"https://pith.science/api/pith-number/7E4LOX5JT7XUWQREOZRVRYQJXZ/graph.json","fetch_events":"https://pith.science/api/pith-number/7E4LOX5JT7XUWQREOZRVRYQJXZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7E4LOX5JT7XUWQREOZRVRYQJXZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7E4LOX5JT7XUWQREOZRVRYQJXZ/action/storage_attestation","attest_author":"https://pith.science/pith/7E4LOX5JT7XUWQREOZRVRYQJXZ/action/author_attestation","sign_citation":"https://pith.science/pith/7E4LOX5JT7XUWQREOZRVRYQJXZ/action/citation_signature","submit_replication":"https://pith.science/pith/7E4LOX5JT7XUWQREOZRVRYQJXZ/action/replication_record"}},"created_at":"2026-07-05T09:38:27.877577+00:00","updated_at":"2026-07-05T09:38:27.877577+00:00"}