{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:PVPXFOEBALL75FBEMG57TOU7I4","short_pith_number":"pith:PVPXFOEB","schema_version":"1.0","canonical_sha256":"7d5f72b88102d7fe942461bbf9ba9f4721f129b795b5ee911fa7790633ff2fc1","source":{"kind":"arxiv","id":"2204.07350","version":1},"attestation_state":"computed","paper":{"title":"Condition-Invariant and Compact Visual Place Description by Convolutional Autoencoder","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.RO"],"primary_cat":"cs.CV","authors_text":"Hanjing Ye, Hong Zhang, Jingwen Yu, Li He, Weinan Chen, Yisheng Guan","submitted_at":"2022-04-15T07:09:23Z","abstract_excerpt":"Visual place recognition (VPR) in condition-varying environments is still an open problem. Popular solutions are CNN-based image descriptors, which have been shown to outperform traditional image descriptors based on hand-crafted visual features. However, there are two drawbacks of current CNN-based descriptors: a) their high dimension and b) lack of generalization, leading to low efficiency and poor performance in applications. In this paper, we propose to use a convolutional autoencoder (CAE) to tackle this problem. We employ a high-level layer of a pre-trained CNN to generate features, and "},"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":"2204.07350","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-04-15T07:09:23Z","cross_cats_sorted":["cs.RO"],"title_canon_sha256":"c46a99d55d00380c0e5bce94541924edcd0622ea7e792202c24ac18fd381bac9","abstract_canon_sha256":"de469aee7fc4174994d21bc17ae701c2a96346be6da9cf0c7703e08ee733eeba"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:15:03.059262Z","signature_b64":"xKNtLAWmKYsVVC7RTA/dyat/UgQOqKOdwxtJw+4gDTteENsA6/Btgntlv2vXPWyETj8MsbxqMHoCobs9TywkBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7d5f72b88102d7fe942461bbf9ba9f4721f129b795b5ee911fa7790633ff2fc1","last_reissued_at":"2026-07-05T04:15:03.058858Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:15:03.058858Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Condition-Invariant and Compact Visual Place Description by Convolutional Autoencoder","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.RO"],"primary_cat":"cs.CV","authors_text":"Hanjing Ye, Hong Zhang, Jingwen Yu, Li He, Weinan Chen, Yisheng Guan","submitted_at":"2022-04-15T07:09:23Z","abstract_excerpt":"Visual place recognition (VPR) in condition-varying environments is still an open problem. Popular solutions are CNN-based image descriptors, which have been shown to outperform traditional image descriptors based on hand-crafted visual features. However, there are two drawbacks of current CNN-based descriptors: a) their high dimension and b) lack of generalization, leading to low efficiency and poor performance in applications. In this paper, we propose to use a convolutional autoencoder (CAE) to tackle this problem. We employ a high-level layer of a pre-trained CNN to generate features, and "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2204.07350","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/2204.07350/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":"2204.07350","created_at":"2026-07-05T04:15:03.058911+00:00"},{"alias_kind":"arxiv_version","alias_value":"2204.07350v1","created_at":"2026-07-05T04:15:03.058911+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2204.07350","created_at":"2026-07-05T04:15:03.058911+00:00"},{"alias_kind":"pith_short_12","alias_value":"PVPXFOEBALL7","created_at":"2026-07-05T04:15:03.058911+00:00"},{"alias_kind":"pith_short_16","alias_value":"PVPXFOEBALL75FBE","created_at":"2026-07-05T04:15:03.058911+00:00"},{"alias_kind":"pith_short_8","alias_value":"PVPXFOEB","created_at":"2026-07-05T04:15:03.058911+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/PVPXFOEBALL75FBEMG57TOU7I4","json":"https://pith.science/pith/PVPXFOEBALL75FBEMG57TOU7I4.json","graph_json":"https://pith.science/api/pith-number/PVPXFOEBALL75FBEMG57TOU7I4/graph.json","events_json":"https://pith.science/api/pith-number/PVPXFOEBALL75FBEMG57TOU7I4/events.json","paper":"https://pith.science/paper/PVPXFOEB"},"agent_actions":{"view_html":"https://pith.science/pith/PVPXFOEBALL75FBEMG57TOU7I4","download_json":"https://pith.science/pith/PVPXFOEBALL75FBEMG57TOU7I4.json","view_paper":"https://pith.science/paper/PVPXFOEB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2204.07350&json=true","fetch_graph":"https://pith.science/api/pith-number/PVPXFOEBALL75FBEMG57TOU7I4/graph.json","fetch_events":"https://pith.science/api/pith-number/PVPXFOEBALL75FBEMG57TOU7I4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PVPXFOEBALL75FBEMG57TOU7I4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PVPXFOEBALL75FBEMG57TOU7I4/action/storage_attestation","attest_author":"https://pith.science/pith/PVPXFOEBALL75FBEMG57TOU7I4/action/author_attestation","sign_citation":"https://pith.science/pith/PVPXFOEBALL75FBEMG57TOU7I4/action/citation_signature","submit_replication":"https://pith.science/pith/PVPXFOEBALL75FBEMG57TOU7I4/action/replication_record"}},"created_at":"2026-07-05T04:15:03.058911+00:00","updated_at":"2026-07-05T04:15:03.058911+00:00"}