{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:E3N4RNGK2GND2P4QCYTU45BZGW","short_pith_number":"pith:E3N4RNGK","canonical_record":{"source":{"id":"2406.09637","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2024-06-14T00:06:52Z","cross_cats_sorted":[],"title_canon_sha256":"612eecd5e249551934b0b8d6874c7d691ddfe940390f6f733f44bf8af9558a99","abstract_canon_sha256":"ab8582984c1951e8628863d3b4c132cc5e7e81917f1a2539531653c9c0c09829"},"schema_version":"1.0"},"canonical_sha256":"26dbc8b4cad19a3d3f9016274e7439358338d46e37b5413205008a5cc7fd49ea","source":{"kind":"arxiv","id":"2406.09637","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2406.09637","created_at":"2026-07-05T08:31:57Z"},{"alias_kind":"arxiv_version","alias_value":"2406.09637v1","created_at":"2026-07-05T08:31:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.09637","created_at":"2026-07-05T08:31:57Z"},{"alias_kind":"pith_short_12","alias_value":"E3N4RNGK2GND","created_at":"2026-07-05T08:31:57Z"},{"alias_kind":"pith_short_16","alias_value":"E3N4RNGK2GND2P4Q","created_at":"2026-07-05T08:31:57Z"},{"alias_kind":"pith_short_8","alias_value":"E3N4RNGK","created_at":"2026-07-05T08:31:57Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:E3N4RNGK2GND2P4QCYTU45BZGW","target":"record","payload":{"canonical_record":{"source":{"id":"2406.09637","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2024-06-14T00:06:52Z","cross_cats_sorted":[],"title_canon_sha256":"612eecd5e249551934b0b8d6874c7d691ddfe940390f6f733f44bf8af9558a99","abstract_canon_sha256":"ab8582984c1951e8628863d3b4c132cc5e7e81917f1a2539531653c9c0c09829"},"schema_version":"1.0"},"canonical_sha256":"26dbc8b4cad19a3d3f9016274e7439358338d46e37b5413205008a5cc7fd49ea","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:31:57.417383Z","signature_b64":"c1DeHag+poIGKUQpraawJQSuJQ6qKDoJMVjn+dv/Put5oU9Og3I2VeYBx0COEFGVQnMK0Gcv6QMAhP5+MQvHAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"26dbc8b4cad19a3d3f9016274e7439358338d46e37b5413205008a5cc7fd49ea","last_reissued_at":"2026-07-05T08:31:57.416864Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:31:57.416864Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2406.09637","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":"2026-07-05T08:31:57Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"L7+CYcjfaldmpH1w70A4JF9wwsMaKxqUrHE2sBIUw5CmOj5tAUmx3PuR5nxT9c0dg5868HFSG7H/l93UlVjgCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T06:29:01.186066Z"},"content_sha256":"4593453413e9b09cf36354b5f51a64b74c7fbe0fbe6569ed1a360e833eea90cd","schema_version":"1.0","event_id":"sha256:4593453413e9b09cf36354b5f51a64b74c7fbe0fbe6569ed1a360e833eea90cd"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:E3N4RNGK2GND2P4QCYTU45BZGW","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Industrial Language-Image Dataset (ILID): Adapting Vision Foundation Models for Industrial Settings","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Duc Trung Thieu, Julian Koch, Keno Moenck, Thorsten Sch\\\"uppstuhl","submitted_at":"2024-06-14T00:06:52Z","abstract_excerpt":"In recent years, the upstream of Large Language Models (LLM) has also encouraged the computer vision community to work on substantial multimodal datasets and train models on a scale in a self-/semi-supervised manner, resulting in Vision Foundation Models (VFM), as, e.g., Contrastive Language-Image Pre-training (CLIP). The models generalize well and perform outstandingly on everyday objects or scenes, even on downstream tasks, tasks the model has not been trained on, while the application in specialized domains, as in an industrial context, is still an open research question. Here, fine-tuning "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.09637","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/2406.09637/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":"2026-07-05T08:31:57Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ApobqM5Kb5VY6sS+ScoWHRJTigdIpWNGiI2WhqQZKN0+Xo0boNfm0tScReDtGicfailx0VjczdTGWU/1Kb65DQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T06:29:01.186879Z"},"content_sha256":"3cc2862ea8ea6769bae5434a044a85017ad8480f146fce0af95cb6652fc4e0e6","schema_version":"1.0","event_id":"sha256:3cc2862ea8ea6769bae5434a044a85017ad8480f146fce0af95cb6652fc4e0e6"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/E3N4RNGK2GND2P4QCYTU45BZGW/bundle.json","state_url":"https://pith.science/pith/E3N4RNGK2GND2P4QCYTU45BZGW/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/E3N4RNGK2GND2P4QCYTU45BZGW/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":"2026-08-10T06:29:01Z","links":{"resolver":"https://pith.science/pith/E3N4RNGK2GND2P4QCYTU45BZGW","bundle":"https://pith.science/pith/E3N4RNGK2GND2P4QCYTU45BZGW/bundle.json","state":"https://pith.science/pith/E3N4RNGK2GND2P4QCYTU45BZGW/state.json","well_known_bundle":"https://pith.science/.well-known/pith/E3N4RNGK2GND2P4QCYTU45BZGW/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:E3N4RNGK2GND2P4QCYTU45BZGW","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":"ab8582984c1951e8628863d3b4c132cc5e7e81917f1a2539531653c9c0c09829","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2024-06-14T00:06:52Z","title_canon_sha256":"612eecd5e249551934b0b8d6874c7d691ddfe940390f6f733f44bf8af9558a99"},"schema_version":"1.0","source":{"id":"2406.09637","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2406.09637","created_at":"2026-07-05T08:31:57Z"},{"alias_kind":"arxiv_version","alias_value":"2406.09637v1","created_at":"2026-07-05T08:31:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.09637","created_at":"2026-07-05T08:31:57Z"},{"alias_kind":"pith_short_12","alias_value":"E3N4RNGK2GND","created_at":"2026-07-05T08:31:57Z"},{"alias_kind":"pith_short_16","alias_value":"E3N4RNGK2GND2P4Q","created_at":"2026-07-05T08:31:57Z"},{"alias_kind":"pith_short_8","alias_value":"E3N4RNGK","created_at":"2026-07-05T08:31:57Z"}],"graph_snapshots":[{"event_id":"sha256:3cc2862ea8ea6769bae5434a044a85017ad8480f146fce0af95cb6652fc4e0e6","target":"graph","created_at":"2026-07-05T08:31:57Z","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/2406.09637/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In recent years, the upstream of Large Language Models (LLM) has also encouraged the computer vision community to work on substantial multimodal datasets and train models on a scale in a self-/semi-supervised manner, resulting in Vision Foundation Models (VFM), as, e.g., Contrastive Language-Image Pre-training (CLIP). The models generalize well and perform outstandingly on everyday objects or scenes, even on downstream tasks, tasks the model has not been trained on, while the application in specialized domains, as in an industrial context, is still an open research question. Here, fine-tuning ","authors_text":"Duc Trung Thieu, Julian Koch, Keno Moenck, Thorsten Sch\\\"uppstuhl","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2024-06-14T00:06:52Z","title":"Industrial Language-Image Dataset (ILID): Adapting Vision Foundation Models for Industrial Settings"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.09637","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":"sha256:4593453413e9b09cf36354b5f51a64b74c7fbe0fbe6569ed1a360e833eea90cd","target":"record","created_at":"2026-07-05T08:31:57Z","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":"ab8582984c1951e8628863d3b4c132cc5e7e81917f1a2539531653c9c0c09829","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2024-06-14T00:06:52Z","title_canon_sha256":"612eecd5e249551934b0b8d6874c7d691ddfe940390f6f733f44bf8af9558a99"},"schema_version":"1.0","source":{"id":"2406.09637","kind":"arxiv","version":1}},"canonical_sha256":"26dbc8b4cad19a3d3f9016274e7439358338d46e37b5413205008a5cc7fd49ea","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"26dbc8b4cad19a3d3f9016274e7439358338d46e37b5413205008a5cc7fd49ea","first_computed_at":"2026-07-05T08:31:57.416864Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:31:57.416864Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"c1DeHag+poIGKUQpraawJQSuJQ6qKDoJMVjn+dv/Put5oU9Og3I2VeYBx0COEFGVQnMK0Gcv6QMAhP5+MQvHAg==","signature_status":"signed_v1","signed_at":"2026-07-05T08:31:57.417383Z","signed_message":"canonical_sha256_bytes"},"source_id":"2406.09637","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4593453413e9b09cf36354b5f51a64b74c7fbe0fbe6569ed1a360e833eea90cd","sha256:3cc2862ea8ea6769bae5434a044a85017ad8480f146fce0af95cb6652fc4e0e6"],"state_sha256":"3540872208aecef10f539097573a8583252ce7cc44a7c9fd2c14ff698d248630"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"eDbvrlWalj6BFuRFkrKLUbQ2fRWQbWCDTdrX4M9++It67XU6cVYY0GQIf/66syqGEoTKVXyVqHJDfrojLjxTCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-10T06:29:01.191148Z","bundle_sha256":"bb0e01933dad4f5296a4303ff3c0d3b46506e11e213e3c54121223b854bcac50"}}