{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:UPYB3WCYQWSZ7ANYSDEISI7KIP","short_pith_number":"pith:UPYB3WCY","schema_version":"1.0","canonical_sha256":"a3f01dd85885a59f81b890c88923ea43ec7dd785d8d09c6f3cf0052ae7f53a5d","source":{"kind":"arxiv","id":"2605.16397","version":1},"attestation_state":"computed","paper":{"title":"Trajectory-Aware Adaptive Inference in Object Detection Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Dimitris Zissis, Giannis Spiliopoulos, Grigorios Papanikolaou, Ioannis Kontopoulos, Konstantinos Tserpes","submitted_at":"2026-05-12T16:04:07Z","abstract_excerpt":"The increasing integration of sensors in autonomous maritime navigation has led to large-scale multimodal datasets, raising challenges in achieving efficient real-time perception. In such systems, object detection and trajectory perception of nearby vessels are tightly coupled, particularly in dynamic environments such as maritime navigation. However, the efficiency of object detection models during inference remains an often-overlooked aspect. To this end, we build upon an existing object detection framework by incorporating GPS trajectory data into the inference process to enable input-adapt"},"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":"2605.16397","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-05-12T16:04:07Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"02db44f3b82c0f2d1156fc6ee9f8d5d50820c070a8a09e6470c7b92f9532f637","abstract_canon_sha256":"c3b63e7024e2e94b5695c03f69da6340feb08cba6918d26c21ac3deb69190006"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T00:02:20.284603Z","signature_b64":"S+FZDyx+2RwQF7inIxkrteI+sM858nfQDbXnhJkdGtEdaBbiOk6UkdwHAQyovaS2yMx8id1OTn0rp4+KwnX9BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a3f01dd85885a59f81b890c88923ea43ec7dd785d8d09c6f3cf0052ae7f53a5d","last_reissued_at":"2026-05-20T00:02:20.283949Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T00:02:20.283949Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Trajectory-Aware Adaptive Inference in Object Detection Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Dimitris Zissis, Giannis Spiliopoulos, Grigorios Papanikolaou, Ioannis Kontopoulos, Konstantinos Tserpes","submitted_at":"2026-05-12T16:04:07Z","abstract_excerpt":"The increasing integration of sensors in autonomous maritime navigation has led to large-scale multimodal datasets, raising challenges in achieving efficient real-time perception. In such systems, object detection and trajectory perception of nearby vessels are tightly coupled, particularly in dynamic environments such as maritime navigation. However, the efficiency of object detection models during inference remains an often-overlooked aspect. To this end, we build upon an existing object detection framework by incorporating GPS trajectory data into the inference process to enable input-adapt"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.16397","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/2605.16397/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"claim_evidence","ran_at":"2026-05-19T22:41:58.277130Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"ai_meta_artifact","ran_at":"2026-05-19T19:34:36.604630Z","status":"skipped","version":"1.0.0","findings_count":0}],"snapshot_sha256":"6c7c03699628c5eafc2c32d250ed506ca990d86e3ff2f3cd0b34145b0a1da8ff"},"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":"2605.16397","created_at":"2026-05-20T00:02:20.284042+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.16397v1","created_at":"2026-05-20T00:02:20.284042+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.16397","created_at":"2026-05-20T00:02:20.284042+00:00"},{"alias_kind":"pith_short_12","alias_value":"UPYB3WCYQWSZ","created_at":"2026-05-20T00:02:20.284042+00:00"},{"alias_kind":"pith_short_16","alias_value":"UPYB3WCYQWSZ7ANY","created_at":"2026-05-20T00:02:20.284042+00:00"},{"alias_kind":"pith_short_8","alias_value":"UPYB3WCY","created_at":"2026-05-20T00:02:20.284042+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/UPYB3WCYQWSZ7ANYSDEISI7KIP","json":"https://pith.science/pith/UPYB3WCYQWSZ7ANYSDEISI7KIP.json","graph_json":"https://pith.science/api/pith-number/UPYB3WCYQWSZ7ANYSDEISI7KIP/graph.json","events_json":"https://pith.science/api/pith-number/UPYB3WCYQWSZ7ANYSDEISI7KIP/events.json","paper":"https://pith.science/paper/UPYB3WCY"},"agent_actions":{"view_html":"https://pith.science/pith/UPYB3WCYQWSZ7ANYSDEISI7KIP","download_json":"https://pith.science/pith/UPYB3WCYQWSZ7ANYSDEISI7KIP.json","view_paper":"https://pith.science/paper/UPYB3WCY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.16397&json=true","fetch_graph":"https://pith.science/api/pith-number/UPYB3WCYQWSZ7ANYSDEISI7KIP/graph.json","fetch_events":"https://pith.science/api/pith-number/UPYB3WCYQWSZ7ANYSDEISI7KIP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UPYB3WCYQWSZ7ANYSDEISI7KIP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UPYB3WCYQWSZ7ANYSDEISI7KIP/action/storage_attestation","attest_author":"https://pith.science/pith/UPYB3WCYQWSZ7ANYSDEISI7KIP/action/author_attestation","sign_citation":"https://pith.science/pith/UPYB3WCYQWSZ7ANYSDEISI7KIP/action/citation_signature","submit_replication":"https://pith.science/pith/UPYB3WCYQWSZ7ANYSDEISI7KIP/action/replication_record"}},"created_at":"2026-05-20T00:02:20.284042+00:00","updated_at":"2026-05-20T00:02:20.284042+00:00"}