{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:K3WVKAXBJ3WN6J47O4Q57EGVJV","short_pith_number":"pith:K3WVKAXB","schema_version":"1.0","canonical_sha256":"56ed5502e14eecdf279f7721df90d54d4d09a63ddd8e3ee7a1f71afebcc630b9","source":{"kind":"arxiv","id":"1812.06873","version":1},"attestation_state":"computed","paper":{"title":"Learning Common Representation from RGB and Depth Images","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Boris Chidlovskii, Giorgio Giannone","submitted_at":"2018-12-17T16:22:47Z","abstract_excerpt":"We propose a new deep learning architecture for the tasks of semantic segmentation and depth prediction from RGB-D images. We revise the state of art based on the RGB and depth feature fusion, where both modalities are assumed to be available at train and test time. We propose a new architecture where the feature fusion is replaced with a common deep representation. Combined with an encoder-decoder type of the network, the architecture can jointly learn models for semantic segmentation and depth estimation based on their common representation. This representation, inspired by multi-view learni"},"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":"1812.06873","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2018-12-17T16:22:47Z","cross_cats_sorted":[],"title_canon_sha256":"2e09ca45773baec7e9695ab80edbf9d011eb35c1c577b7975631da19806cb955","abstract_canon_sha256":"09153e4b3ab4282babfcdc434e1e025f56b3a65241e429bfc398c9f3f8ae0ccb"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:58:09.165187Z","signature_b64":"JOKEG2QZEJCZrnDG8LtVE1yafunD3z6a69ngCph1cvrSDjF0Ym0chNERYaymXkFAFNquS9gWlR6Z+8NIG0ZOAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"56ed5502e14eecdf279f7721df90d54d4d09a63ddd8e3ee7a1f71afebcc630b9","last_reissued_at":"2026-05-17T23:58:09.164551Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:58:09.164551Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning Common Representation from RGB and Depth Images","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Boris Chidlovskii, Giorgio Giannone","submitted_at":"2018-12-17T16:22:47Z","abstract_excerpt":"We propose a new deep learning architecture for the tasks of semantic segmentation and depth prediction from RGB-D images. We revise the state of art based on the RGB and depth feature fusion, where both modalities are assumed to be available at train and test time. We propose a new architecture where the feature fusion is replaced with a common deep representation. Combined with an encoder-decoder type of the network, the architecture can jointly learn models for semantic segmentation and depth estimation based on their common representation. This representation, inspired by multi-view learni"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1812.06873","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":""},"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":"1812.06873","created_at":"2026-05-17T23:58:09.164650+00:00"},{"alias_kind":"arxiv_version","alias_value":"1812.06873v1","created_at":"2026-05-17T23:58:09.164650+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1812.06873","created_at":"2026-05-17T23:58:09.164650+00:00"},{"alias_kind":"pith_short_12","alias_value":"K3WVKAXBJ3WN","created_at":"2026-05-18T12:32:33.847187+00:00"},{"alias_kind":"pith_short_16","alias_value":"K3WVKAXBJ3WN6J47","created_at":"2026-05-18T12:32:33.847187+00:00"},{"alias_kind":"pith_short_8","alias_value":"K3WVKAXB","created_at":"2026-05-18T12:32:33.847187+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/K3WVKAXBJ3WN6J47O4Q57EGVJV","json":"https://pith.science/pith/K3WVKAXBJ3WN6J47O4Q57EGVJV.json","graph_json":"https://pith.science/api/pith-number/K3WVKAXBJ3WN6J47O4Q57EGVJV/graph.json","events_json":"https://pith.science/api/pith-number/K3WVKAXBJ3WN6J47O4Q57EGVJV/events.json","paper":"https://pith.science/paper/K3WVKAXB"},"agent_actions":{"view_html":"https://pith.science/pith/K3WVKAXBJ3WN6J47O4Q57EGVJV","download_json":"https://pith.science/pith/K3WVKAXBJ3WN6J47O4Q57EGVJV.json","view_paper":"https://pith.science/paper/K3WVKAXB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1812.06873&json=true","fetch_graph":"https://pith.science/api/pith-number/K3WVKAXBJ3WN6J47O4Q57EGVJV/graph.json","fetch_events":"https://pith.science/api/pith-number/K3WVKAXBJ3WN6J47O4Q57EGVJV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/K3WVKAXBJ3WN6J47O4Q57EGVJV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/K3WVKAXBJ3WN6J47O4Q57EGVJV/action/storage_attestation","attest_author":"https://pith.science/pith/K3WVKAXBJ3WN6J47O4Q57EGVJV/action/author_attestation","sign_citation":"https://pith.science/pith/K3WVKAXBJ3WN6J47O4Q57EGVJV/action/citation_signature","submit_replication":"https://pith.science/pith/K3WVKAXBJ3WN6J47O4Q57EGVJV/action/replication_record"}},"created_at":"2026-05-17T23:58:09.164650+00:00","updated_at":"2026-05-17T23:58:09.164650+00:00"}