{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:BRIE6BRHJFRK4NCXADDLRAR2MP","short_pith_number":"pith:BRIE6BRH","schema_version":"1.0","canonical_sha256":"0c504f06274962ae345700c6b8823a63fc194dccd69d645c878d08041766098d","source":{"kind":"arxiv","id":"2312.06736","version":3},"attestation_state":"computed","paper":{"title":"SqueezeSAM: User friendly mobile interactive segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Balakrishnan Varadarajan, Bilge Soran, Chenchen Zhu, Forrest Iandola, Lemeng Wu, Raghuraman Krishnamoorthi, Vikas Chandra, Xiaoyu Xiang, Yunyang Xiong","submitted_at":"2023-12-11T16:04:22Z","abstract_excerpt":"The Segment Anything Model (SAM) has been a cornerstone in the field of interactive segmentation, propelling significant progress in generative AI, computational photography, and medical imaging. Despite its ability to process arbitrary user input and generate corresponding segmentation masks, SAM's 600 million parameter architecture, based on ViT-H, is not compatible with current mobile hardware due to its high computational demands and large model size. Our research aims to adapt SAM for use in mobile photography applications. To this end, we have developed a fully convolutional SqueezeSAM m"},"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":"2312.06736","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-12-11T16:04:22Z","cross_cats_sorted":[],"title_canon_sha256":"ab10458d18587b6225753cd02cf57f3d3815b5caa23c2f2990ddb9d988d1313d","abstract_canon_sha256":"a2acdb4f2abae915968e90afe227f18a1b8a8114aa2f7493fa2b1e3c855e9d35"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:21:10.500904Z","signature_b64":"2XP3RO5m/QfIwMXy0dtH5IlnylbwoDiZ7M31s6MykDnEpFTndi8CyNMZ5oE8lChR4HEsrnbuRivUVWT5csKZCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0c504f06274962ae345700c6b8823a63fc194dccd69d645c878d08041766098d","last_reissued_at":"2026-07-05T08:21:10.500446Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:21:10.500446Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SqueezeSAM: User friendly mobile interactive segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Balakrishnan Varadarajan, Bilge Soran, Chenchen Zhu, Forrest Iandola, Lemeng Wu, Raghuraman Krishnamoorthi, Vikas Chandra, Xiaoyu Xiang, Yunyang Xiong","submitted_at":"2023-12-11T16:04:22Z","abstract_excerpt":"The Segment Anything Model (SAM) has been a cornerstone in the field of interactive segmentation, propelling significant progress in generative AI, computational photography, and medical imaging. Despite its ability to process arbitrary user input and generate corresponding segmentation masks, SAM's 600 million parameter architecture, based on ViT-H, is not compatible with current mobile hardware due to its high computational demands and large model size. Our research aims to adapt SAM for use in mobile photography applications. To this end, we have developed a fully convolutional SqueezeSAM m"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.06736","kind":"arxiv","version":3},"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/2312.06736/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":"2312.06736","created_at":"2026-07-05T08:21:10.500501+00:00"},{"alias_kind":"arxiv_version","alias_value":"2312.06736v3","created_at":"2026-07-05T08:21:10.500501+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.06736","created_at":"2026-07-05T08:21:10.500501+00:00"},{"alias_kind":"pith_short_12","alias_value":"BRIE6BRHJFRK","created_at":"2026-07-05T08:21:10.500501+00:00"},{"alias_kind":"pith_short_16","alias_value":"BRIE6BRHJFRK4NCX","created_at":"2026-07-05T08:21:10.500501+00:00"},{"alias_kind":"pith_short_8","alias_value":"BRIE6BRH","created_at":"2026-07-05T08:21:10.500501+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2410.04960","citing_title":"On Efficient Variants of Segment Anything Model: A Survey","ref_index":42,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/BRIE6BRHJFRK4NCXADDLRAR2MP","json":"https://pith.science/pith/BRIE6BRHJFRK4NCXADDLRAR2MP.json","graph_json":"https://pith.science/api/pith-number/BRIE6BRHJFRK4NCXADDLRAR2MP/graph.json","events_json":"https://pith.science/api/pith-number/BRIE6BRHJFRK4NCXADDLRAR2MP/events.json","paper":"https://pith.science/paper/BRIE6BRH"},"agent_actions":{"view_html":"https://pith.science/pith/BRIE6BRHJFRK4NCXADDLRAR2MP","download_json":"https://pith.science/pith/BRIE6BRHJFRK4NCXADDLRAR2MP.json","view_paper":"https://pith.science/paper/BRIE6BRH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2312.06736&json=true","fetch_graph":"https://pith.science/api/pith-number/BRIE6BRHJFRK4NCXADDLRAR2MP/graph.json","fetch_events":"https://pith.science/api/pith-number/BRIE6BRHJFRK4NCXADDLRAR2MP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BRIE6BRHJFRK4NCXADDLRAR2MP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BRIE6BRHJFRK4NCXADDLRAR2MP/action/storage_attestation","attest_author":"https://pith.science/pith/BRIE6BRHJFRK4NCXADDLRAR2MP/action/author_attestation","sign_citation":"https://pith.science/pith/BRIE6BRHJFRK4NCXADDLRAR2MP/action/citation_signature","submit_replication":"https://pith.science/pith/BRIE6BRHJFRK4NCXADDLRAR2MP/action/replication_record"}},"created_at":"2026-07-05T08:21:10.500501+00:00","updated_at":"2026-07-05T08:21:10.500501+00:00"}