{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:7425OYDBXUQK3XALJK2NZBEBXP","short_pith_number":"pith:7425OYDB","canonical_record":{"source":{"id":"2402.14123","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-02-21T20:43:49Z","cross_cats_sorted":["cs.AI","cs.CV"],"title_canon_sha256":"1518b16fa0449d1c6fcdcc65d99ee6ddd6c8b7a978d3138f7afeca4b99723187","abstract_canon_sha256":"7aed73d4e7d3ff49c0a5a160bd3b83334b100d9386b86d6a28c2aaf21211cf11"},"schema_version":"1.0"},"canonical_sha256":"ff35d76061bd20addc0b4ab4dc8481bbe0036edec8b555a0d362536004fa631b","source":{"kind":"arxiv","id":"2402.14123","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2402.14123","created_at":"2026-07-05T09:44:49Z"},{"alias_kind":"arxiv_version","alias_value":"2402.14123v2","created_at":"2026-07-05T09:44:49Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.14123","created_at":"2026-07-05T09:44:49Z"},{"alias_kind":"pith_short_12","alias_value":"7425OYDBXUQK","created_at":"2026-07-05T09:44:49Z"},{"alias_kind":"pith_short_16","alias_value":"7425OYDBXUQK3XAL","created_at":"2026-07-05T09:44:49Z"},{"alias_kind":"pith_short_8","alias_value":"7425OYDB","created_at":"2026-07-05T09:44:49Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:7425OYDBXUQK3XALJK2NZBEBXP","target":"record","payload":{"canonical_record":{"source":{"id":"2402.14123","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-02-21T20:43:49Z","cross_cats_sorted":["cs.AI","cs.CV"],"title_canon_sha256":"1518b16fa0449d1c6fcdcc65d99ee6ddd6c8b7a978d3138f7afeca4b99723187","abstract_canon_sha256":"7aed73d4e7d3ff49c0a5a160bd3b83334b100d9386b86d6a28c2aaf21211cf11"},"schema_version":"1.0"},"canonical_sha256":"ff35d76061bd20addc0b4ab4dc8481bbe0036edec8b555a0d362536004fa631b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:44:49.676423Z","signature_b64":"86Mo9hCcNGjyIy7RrT8awmSDP569XdDTRS5Mo07N/9rf/hhDFg6Pt11DVw0asFL90OOi/olBsutuDJkWFqtcBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ff35d76061bd20addc0b4ab4dc8481bbe0036edec8b555a0d362536004fa631b","last_reissued_at":"2026-07-05T09:44:49.675885Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:44:49.675885Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2402.14123","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:44:49Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"eX09lwx6woV90Lulu6u6AZM1y3nPlADOeacu4aqTbU9kxDFwx2iburOshlW+ZMQX1wlZjwhiD5Ylni3if9fLDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-21T05:23:21.931216Z"},"content_sha256":"6b7175d1bcdb266afc499ce146adbd56f2cf9e7a6c40c26022054a1cd149d240","schema_version":"1.0","event_id":"sha256:6b7175d1bcdb266afc499ce146adbd56f2cf9e7a6c40c26022054a1cd149d240"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:7425OYDBXUQK3XALJK2NZBEBXP","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"DeiSAM: Segment Anything with Deictic Prompting","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CV"],"primary_cat":"cs.LG","authors_text":"Devendra Singh Dhami, Gopika Sudhakaran, Hikaru Shindo, Kristian Kersting, Manuel Brack, Patrick Schramowski","submitted_at":"2024-02-21T20:43:49Z","abstract_excerpt":"Large-scale, pre-trained neural networks have demonstrated strong capabilities in various tasks, including zero-shot image segmentation. To identify concrete objects in complex scenes, humans instinctively rely on deictic descriptions in natural language, i.e., referring to something depending on the context such as \"The object that is on the desk and behind the cup.\". However, deep learning approaches cannot reliably interpret such deictic representations due to their lack of reasoning capabilities in complex scenarios. To remedy this issue, we propose DeiSAM -- a combination of large pre-tra"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.14123","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/2402.14123/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":"2026-07-05T09:44:49Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nTo06BpxYyr5n9r3xg32vUnFn4UmuxuxeAjleP+1rS5m4Dx1p4+R706inIbOQKK9rh1lmRDR/mls40fT64KwCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-21T05:23:21.931613Z"},"content_sha256":"d49da022be850fbdfce080410e33b7d9ebc18fbea6632855b07028a40f506813","schema_version":"1.0","event_id":"sha256:d49da022be850fbdfce080410e33b7d9ebc18fbea6632855b07028a40f506813"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/7425OYDBXUQK3XALJK2NZBEBXP/bundle.json","state_url":"https://pith.science/pith/7425OYDBXUQK3XALJK2NZBEBXP/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/7425OYDBXUQK3XALJK2NZBEBXP/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":"2026-07-21T05:23:21Z","links":{"resolver":"https://pith.science/pith/7425OYDBXUQK3XALJK2NZBEBXP","bundle":"https://pith.science/pith/7425OYDBXUQK3XALJK2NZBEBXP/bundle.json","state":"https://pith.science/pith/7425OYDBXUQK3XALJK2NZBEBXP/state.json","well_known_bundle":"https://pith.science/.well-known/pith/7425OYDBXUQK3XALJK2NZBEBXP/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:7425OYDBXUQK3XALJK2NZBEBXP","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":"7aed73d4e7d3ff49c0a5a160bd3b83334b100d9386b86d6a28c2aaf21211cf11","cross_cats_sorted":["cs.AI","cs.CV"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-02-21T20:43:49Z","title_canon_sha256":"1518b16fa0449d1c6fcdcc65d99ee6ddd6c8b7a978d3138f7afeca4b99723187"},"schema_version":"1.0","source":{"id":"2402.14123","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2402.14123","created_at":"2026-07-05T09:44:49Z"},{"alias_kind":"arxiv_version","alias_value":"2402.14123v2","created_at":"2026-07-05T09:44:49Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.14123","created_at":"2026-07-05T09:44:49Z"},{"alias_kind":"pith_short_12","alias_value":"7425OYDBXUQK","created_at":"2026-07-05T09:44:49Z"},{"alias_kind":"pith_short_16","alias_value":"7425OYDBXUQK3XAL","created_at":"2026-07-05T09:44:49Z"},{"alias_kind":"pith_short_8","alias_value":"7425OYDB","created_at":"2026-07-05T09:44:49Z"}],"graph_snapshots":[{"event_id":"sha256:d49da022be850fbdfce080410e33b7d9ebc18fbea6632855b07028a40f506813","target":"graph","created_at":"2026-07-05T09:44:49Z","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/2402.14123/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Large-scale, pre-trained neural networks have demonstrated strong capabilities in various tasks, including zero-shot image segmentation. To identify concrete objects in complex scenes, humans instinctively rely on deictic descriptions in natural language, i.e., referring to something depending on the context such as \"The object that is on the desk and behind the cup.\". However, deep learning approaches cannot reliably interpret such deictic representations due to their lack of reasoning capabilities in complex scenarios. To remedy this issue, we propose DeiSAM -- a combination of large pre-tra","authors_text":"Devendra Singh Dhami, Gopika Sudhakaran, Hikaru Shindo, Kristian Kersting, Manuel Brack, Patrick Schramowski","cross_cats":["cs.AI","cs.CV"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-02-21T20:43:49Z","title":"DeiSAM: Segment Anything with Deictic Prompting"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.14123","kind":"arxiv","version":2},"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":"sha256:6b7175d1bcdb266afc499ce146adbd56f2cf9e7a6c40c26022054a1cd149d240","target":"record","created_at":"2026-07-05T09:44:49Z","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":"7aed73d4e7d3ff49c0a5a160bd3b83334b100d9386b86d6a28c2aaf21211cf11","cross_cats_sorted":["cs.AI","cs.CV"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-02-21T20:43:49Z","title_canon_sha256":"1518b16fa0449d1c6fcdcc65d99ee6ddd6c8b7a978d3138f7afeca4b99723187"},"schema_version":"1.0","source":{"id":"2402.14123","kind":"arxiv","version":2}},"canonical_sha256":"ff35d76061bd20addc0b4ab4dc8481bbe0036edec8b555a0d362536004fa631b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ff35d76061bd20addc0b4ab4dc8481bbe0036edec8b555a0d362536004fa631b","first_computed_at":"2026-07-05T09:44:49.675885Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:44:49.675885Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"86Mo9hCcNGjyIy7RrT8awmSDP569XdDTRS5Mo07N/9rf/hhDFg6Pt11DVw0asFL90OOi/olBsutuDJkWFqtcBA==","signature_status":"signed_v1","signed_at":"2026-07-05T09:44:49.676423Z","signed_message":"canonical_sha256_bytes"},"source_id":"2402.14123","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:6b7175d1bcdb266afc499ce146adbd56f2cf9e7a6c40c26022054a1cd149d240","sha256:d49da022be850fbdfce080410e33b7d9ebc18fbea6632855b07028a40f506813"],"state_sha256":"0e391ceca3e51a5913b5fb038de14105fd7389857839c12310951c77450e28f3"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+TrnH39ZIT/fafX9f+lthmcxIX5ozUAvqXwd1QSL8zVagdSI5Mm1xmlj6mRomiEpz3VIOgYEMwCseBOOyntICQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-21T05:23:21.933684Z","bundle_sha256":"5e9759ac29bb21dfd9c2b7737ce0131d0202b445535432ef54da7cd71edb4970"}}