{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:FRIVMRGYOM6ZCPCVEJRUDMN5V6","short_pith_number":"pith:FRIVMRGY","schema_version":"1.0","canonical_sha256":"2c515644d8733d913c55226341b1bdafb64ff9e64d30e5aba58b8402b2ea1a7a","source":{"kind":"arxiv","id":"2403.03405","version":1},"attestation_state":"computed","paper":{"title":"Causality-based Cross-Modal Representation Learning for Vision-and-Language Navigation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chengju Liu, Huiyi Chen, Liuyi Wang, Qijun Chen, Ronghao Dang, Zongtao He","submitted_at":"2024-03-06T02:01:38Z","abstract_excerpt":"Vision-and-Language Navigation (VLN) has gained significant research interest in recent years due to its potential applications in real-world scenarios. However, existing VLN methods struggle with the issue of spurious associations, resulting in poor generalization with a significant performance gap between seen and unseen environments. In this paper, we tackle this challenge by proposing a unified framework CausalVLN based on the causal learning paradigm to train a robust navigator capable of learning unbiased feature representations. Specifically, we establish reasonable assumptions about co"},"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":"2403.03405","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-03-06T02:01:38Z","cross_cats_sorted":[],"title_canon_sha256":"6f86b85ffd7e9a2cac049156355c5837b2ea4c896219927d0b5560090fb85b66","abstract_canon_sha256":"1fd05aa2c7792efbab5de42dab50da62628e3c6786cd1faa1d247049df9ecf92"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:52:47.703891Z","signature_b64":"BKEgfFjD/wgzbkZ3FqQ/xLiOokfIggPiRurAhP62+x4B/fPHICV5TMfLQhRkbzZd6EbpnI0iDB5b7OsojASQDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2c515644d8733d913c55226341b1bdafb64ff9e64d30e5aba58b8402b2ea1a7a","last_reissued_at":"2026-07-05T07:52:47.703441Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:52:47.703441Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Causality-based Cross-Modal Representation Learning for Vision-and-Language Navigation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chengju Liu, Huiyi Chen, Liuyi Wang, Qijun Chen, Ronghao Dang, Zongtao He","submitted_at":"2024-03-06T02:01:38Z","abstract_excerpt":"Vision-and-Language Navigation (VLN) has gained significant research interest in recent years due to its potential applications in real-world scenarios. However, existing VLN methods struggle with the issue of spurious associations, resulting in poor generalization with a significant performance gap between seen and unseen environments. In this paper, we tackle this challenge by proposing a unified framework CausalVLN based on the causal learning paradigm to train a robust navigator capable of learning unbiased feature representations. Specifically, we establish reasonable assumptions about co"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.03405","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/2403.03405/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":"2403.03405","created_at":"2026-07-05T07:52:47.703503+00:00"},{"alias_kind":"arxiv_version","alias_value":"2403.03405v1","created_at":"2026-07-05T07:52:47.703503+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.03405","created_at":"2026-07-05T07:52:47.703503+00:00"},{"alias_kind":"pith_short_12","alias_value":"FRIVMRGYOM6Z","created_at":"2026-07-05T07:52:47.703503+00:00"},{"alias_kind":"pith_short_16","alias_value":"FRIVMRGYOM6ZCPCV","created_at":"2026-07-05T07:52:47.703503+00:00"},{"alias_kind":"pith_short_8","alias_value":"FRIVMRGY","created_at":"2026-07-05T07:52:47.703503+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/FRIVMRGYOM6ZCPCVEJRUDMN5V6","json":"https://pith.science/pith/FRIVMRGYOM6ZCPCVEJRUDMN5V6.json","graph_json":"https://pith.science/api/pith-number/FRIVMRGYOM6ZCPCVEJRUDMN5V6/graph.json","events_json":"https://pith.science/api/pith-number/FRIVMRGYOM6ZCPCVEJRUDMN5V6/events.json","paper":"https://pith.science/paper/FRIVMRGY"},"agent_actions":{"view_html":"https://pith.science/pith/FRIVMRGYOM6ZCPCVEJRUDMN5V6","download_json":"https://pith.science/pith/FRIVMRGYOM6ZCPCVEJRUDMN5V6.json","view_paper":"https://pith.science/paper/FRIVMRGY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2403.03405&json=true","fetch_graph":"https://pith.science/api/pith-number/FRIVMRGYOM6ZCPCVEJRUDMN5V6/graph.json","fetch_events":"https://pith.science/api/pith-number/FRIVMRGYOM6ZCPCVEJRUDMN5V6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FRIVMRGYOM6ZCPCVEJRUDMN5V6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FRIVMRGYOM6ZCPCVEJRUDMN5V6/action/storage_attestation","attest_author":"https://pith.science/pith/FRIVMRGYOM6ZCPCVEJRUDMN5V6/action/author_attestation","sign_citation":"https://pith.science/pith/FRIVMRGYOM6ZCPCVEJRUDMN5V6/action/citation_signature","submit_replication":"https://pith.science/pith/FRIVMRGYOM6ZCPCVEJRUDMN5V6/action/replication_record"}},"created_at":"2026-07-05T07:52:47.703503+00:00","updated_at":"2026-07-05T07:52:47.703503+00:00"}