{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:E2LKD6D6G2FZ5KNH3HLC6BOF6K","short_pith_number":"pith:E2LKD6D6","schema_version":"1.0","canonical_sha256":"2696a1f87e368b9ea9a7d9d62f05c5f2a441b133ff827c63a465af38ffede45f","source":{"kind":"arxiv","id":"2504.15728","version":1},"attestation_state":"computed","paper":{"title":"SAGA: Semantic-Aware Gray color Augmentation for Visible-to-Thermal Domain Adaptation across Multi-View Drone and Ground-Based Vision Systems","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Aniruddh Sikdar, Manjunath D, Prajwal Gurunath, Sumanth Udupa, Suresh Sundaram","submitted_at":"2025-04-22T09:22:11Z","abstract_excerpt":"Domain-adaptive thermal object detection plays a key role in facilitating visible (RGB)-to-thermal (IR) adaptation by reducing the need for co-registered image pairs and minimizing reliance on large annotated IR datasets. However, inherent limitations of IR images, such as the lack of color and texture cues, pose challenges for RGB-trained models, leading to increased false positives and poor-quality pseudo-labels. To address this, we propose Semantic-Aware Gray color Augmentation (SAGA), a novel strategy for mitigating color bias and bridging the domain gap by extracting object-level features"},"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":"2504.15728","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2025-04-22T09:22:11Z","cross_cats_sorted":[],"title_canon_sha256":"31ceae241c052f70edefed31400f892584c5378a0732f3065c6f81172df2cd21","abstract_canon_sha256":"64bacfaa99a9d16344045b5a49f0a8e3eb41573a3b3d7a988b1730fe329320c6"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:52:28.993545Z","signature_b64":"mWGKBky32uinSYJUhhLp2QEosjPqSWfT1otZuhvrkN6pReBklM+W6ue9oS6upxlPIsLyjFZpxWXRA5BxuYMDBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2696a1f87e368b9ea9a7d9d62f05c5f2a441b133ff827c63a465af38ffede45f","last_reissued_at":"2026-07-05T10:52:28.993042Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:52:28.993042Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SAGA: Semantic-Aware Gray color Augmentation for Visible-to-Thermal Domain Adaptation across Multi-View Drone and Ground-Based Vision Systems","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Aniruddh Sikdar, Manjunath D, Prajwal Gurunath, Sumanth Udupa, Suresh Sundaram","submitted_at":"2025-04-22T09:22:11Z","abstract_excerpt":"Domain-adaptive thermal object detection plays a key role in facilitating visible (RGB)-to-thermal (IR) adaptation by reducing the need for co-registered image pairs and minimizing reliance on large annotated IR datasets. However, inherent limitations of IR images, such as the lack of color and texture cues, pose challenges for RGB-trained models, leading to increased false positives and poor-quality pseudo-labels. To address this, we propose Semantic-Aware Gray color Augmentation (SAGA), a novel strategy for mitigating color bias and bridging the domain gap by extracting object-level features"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.15728","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/2504.15728/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":"2504.15728","created_at":"2026-07-05T10:52:28.993093+00:00"},{"alias_kind":"arxiv_version","alias_value":"2504.15728v1","created_at":"2026-07-05T10:52:28.993093+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.15728","created_at":"2026-07-05T10:52:28.993093+00:00"},{"alias_kind":"pith_short_12","alias_value":"E2LKD6D6G2FZ","created_at":"2026-07-05T10:52:28.993093+00:00"},{"alias_kind":"pith_short_16","alias_value":"E2LKD6D6G2FZ5KNH","created_at":"2026-07-05T10:52:28.993093+00:00"},{"alias_kind":"pith_short_8","alias_value":"E2LKD6D6","created_at":"2026-07-05T10:52:28.993093+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.03709","citing_title":"AetherVision-Bench: An Open-Vocabulary RGB-Infrared Benchmark for Multi-Angle Segmentation across Aerial and Ground Perspectives","ref_index":24,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/E2LKD6D6G2FZ5KNH3HLC6BOF6K","json":"https://pith.science/pith/E2LKD6D6G2FZ5KNH3HLC6BOF6K.json","graph_json":"https://pith.science/api/pith-number/E2LKD6D6G2FZ5KNH3HLC6BOF6K/graph.json","events_json":"https://pith.science/api/pith-number/E2LKD6D6G2FZ5KNH3HLC6BOF6K/events.json","paper":"https://pith.science/paper/E2LKD6D6"},"agent_actions":{"view_html":"https://pith.science/pith/E2LKD6D6G2FZ5KNH3HLC6BOF6K","download_json":"https://pith.science/pith/E2LKD6D6G2FZ5KNH3HLC6BOF6K.json","view_paper":"https://pith.science/paper/E2LKD6D6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2504.15728&json=true","fetch_graph":"https://pith.science/api/pith-number/E2LKD6D6G2FZ5KNH3HLC6BOF6K/graph.json","fetch_events":"https://pith.science/api/pith-number/E2LKD6D6G2FZ5KNH3HLC6BOF6K/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/E2LKD6D6G2FZ5KNH3HLC6BOF6K/action/timestamp_anchor","attest_storage":"https://pith.science/pith/E2LKD6D6G2FZ5KNH3HLC6BOF6K/action/storage_attestation","attest_author":"https://pith.science/pith/E2LKD6D6G2FZ5KNH3HLC6BOF6K/action/author_attestation","sign_citation":"https://pith.science/pith/E2LKD6D6G2FZ5KNH3HLC6BOF6K/action/citation_signature","submit_replication":"https://pith.science/pith/E2LKD6D6G2FZ5KNH3HLC6BOF6K/action/replication_record"}},"created_at":"2026-07-05T10:52:28.993093+00:00","updated_at":"2026-07-05T10:52:28.993093+00:00"}