{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:WYO5QKQKNLHERYEXVCX57JU4F4","short_pith_number":"pith:WYO5QKQK","schema_version":"1.0","canonical_sha256":"b61dd82a0a6ace48e097a8afdfa69c2f1e2a314e318745232fe2944a01ddd6f7","source":{"kind":"arxiv","id":"2207.11717","version":4},"attestation_state":"computed","paper":{"title":"A Priority Map for Vision-and-Language Navigation with Trajectory Plans and Feature-Location Cues","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Jason Armitage, Leonardo Impett, Rico Sennrich","submitted_at":"2022-07-24T11:09:45Z","abstract_excerpt":"In a busy city street, a pedestrian surrounded by distractions can pick out a single sign if it is relevant to their route. Artificial agents in outdoor Vision-and-Language Navigation (VLN) are also confronted with detecting supervisory signal on environment features and location in inputs. To boost the prominence of relevant features in transformer-based architectures without costly preprocessing and pretraining, we take inspiration from priority maps - a mechanism described in neuropsychological studies. We implement a novel priority map module and pretrain on auxiliary tasks using low-sampl"},"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":"2207.11717","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-07-24T11:09:45Z","cross_cats_sorted":[],"title_canon_sha256":"7b00b05f964e2978875046065d5250b62772ff8c0d80ec1a8d56975dab4f50a1","abstract_canon_sha256":"8af3eb68a6b52a6ef89f48952ae0274d77d0ae2aab0d32d21e3012d0b441dd6e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:17:11.764033Z","signature_b64":"HkkYZ2vby+u8IuFBawGcXU3UHP+HkSd4ICfSKwQqF88eo9GJpHqyA566XITgTsTjhP9591uV7p8QIoFgy5ikDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b61dd82a0a6ace48e097a8afdfa69c2f1e2a314e318745232fe2944a01ddd6f7","last_reissued_at":"2026-07-05T05:17:11.763647Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:17:11.763647Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Priority Map for Vision-and-Language Navigation with Trajectory Plans and Feature-Location Cues","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Jason Armitage, Leonardo Impett, Rico Sennrich","submitted_at":"2022-07-24T11:09:45Z","abstract_excerpt":"In a busy city street, a pedestrian surrounded by distractions can pick out a single sign if it is relevant to their route. Artificial agents in outdoor Vision-and-Language Navigation (VLN) are also confronted with detecting supervisory signal on environment features and location in inputs. To boost the prominence of relevant features in transformer-based architectures without costly preprocessing and pretraining, we take inspiration from priority maps - a mechanism described in neuropsychological studies. We implement a novel priority map module and pretrain on auxiliary tasks using low-sampl"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2207.11717","kind":"arxiv","version":4},"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/2207.11717/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":"2207.11717","created_at":"2026-07-05T05:17:11.763704+00:00"},{"alias_kind":"arxiv_version","alias_value":"2207.11717v4","created_at":"2026-07-05T05:17:11.763704+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2207.11717","created_at":"2026-07-05T05:17:11.763704+00:00"},{"alias_kind":"pith_short_12","alias_value":"WYO5QKQKNLHE","created_at":"2026-07-05T05:17:11.763704+00:00"},{"alias_kind":"pith_short_16","alias_value":"WYO5QKQKNLHERYEX","created_at":"2026-07-05T05:17:11.763704+00:00"},{"alias_kind":"pith_short_8","alias_value":"WYO5QKQK","created_at":"2026-07-05T05:17:11.763704+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2411.08579","citing_title":"NavAgent: Multi-scale Urban Street View Fusion For UAV Embodied Vision-and-Language Navigation","ref_index":2022,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/WYO5QKQKNLHERYEXVCX57JU4F4","json":"https://pith.science/pith/WYO5QKQKNLHERYEXVCX57JU4F4.json","graph_json":"https://pith.science/api/pith-number/WYO5QKQKNLHERYEXVCX57JU4F4/graph.json","events_json":"https://pith.science/api/pith-number/WYO5QKQKNLHERYEXVCX57JU4F4/events.json","paper":"https://pith.science/paper/WYO5QKQK"},"agent_actions":{"view_html":"https://pith.science/pith/WYO5QKQKNLHERYEXVCX57JU4F4","download_json":"https://pith.science/pith/WYO5QKQKNLHERYEXVCX57JU4F4.json","view_paper":"https://pith.science/paper/WYO5QKQK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2207.11717&json=true","fetch_graph":"https://pith.science/api/pith-number/WYO5QKQKNLHERYEXVCX57JU4F4/graph.json","fetch_events":"https://pith.science/api/pith-number/WYO5QKQKNLHERYEXVCX57JU4F4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WYO5QKQKNLHERYEXVCX57JU4F4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WYO5QKQKNLHERYEXVCX57JU4F4/action/storage_attestation","attest_author":"https://pith.science/pith/WYO5QKQKNLHERYEXVCX57JU4F4/action/author_attestation","sign_citation":"https://pith.science/pith/WYO5QKQKNLHERYEXVCX57JU4F4/action/citation_signature","submit_replication":"https://pith.science/pith/WYO5QKQKNLHERYEXVCX57JU4F4/action/replication_record"}},"created_at":"2026-07-05T05:17:11.763704+00:00","updated_at":"2026-07-05T05:17:11.763704+00:00"}