{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:RRGNX7ILHDYW23GE56KHD6WZGE","short_pith_number":"pith:RRGNX7IL","schema_version":"1.0","canonical_sha256":"8c4cdbfd0b38f16d6cc4ef9471fad9312dcc33c54229f8184fc9ee6d71f8a532","source":{"kind":"arxiv","id":"1806.03370","version":1},"attestation_state":"computed","paper":{"title":"Self-supervisory Signals for Object Discovery and Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Alexander Toshev, Etienne Pot, Jana Kosecka","submitted_at":"2018-06-08T22:50:28Z","abstract_excerpt":"In robotic applications, we often face the challenge of discovering new objects while having very little or no labelled training data. In this paper we explore the use of self-supervision provided by a robot traversing an environment to learn representations of encountered objects. Knowledge of ego-motion and depth perception enables the agent to effectively associate multiple object proposals, which serve as training data for learning object representations from unlabelled images. We demonstrate the utility of this representation in two ways. First, we can automatically discover objects by pe"},"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":"1806.03370","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-06-08T22:50:28Z","cross_cats_sorted":[],"title_canon_sha256":"caf98c81bdfce90cfa23fc50351fa3b5425f615fe6d76dd0e23f22d44e31b30f","abstract_canon_sha256":"2f7fad1d152d45d0747bf0730e7c053a171c9d2cff601516920601bb14ce77fa"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:13:43.583840Z","signature_b64":"rpBlZzsv9ic3b2PinQ/v6FYcel8S1MxkFIMoC9ZjbStA9CqHc9bS+4/ptb9DP7P/f5F8qDFjNguzc04wm2AGCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8c4cdbfd0b38f16d6cc4ef9471fad9312dcc33c54229f8184fc9ee6d71f8a532","last_reissued_at":"2026-05-18T00:13:43.583082Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:13:43.583082Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Self-supervisory Signals for Object Discovery and Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Alexander Toshev, Etienne Pot, Jana Kosecka","submitted_at":"2018-06-08T22:50:28Z","abstract_excerpt":"In robotic applications, we often face the challenge of discovering new objects while having very little or no labelled training data. In this paper we explore the use of self-supervision provided by a robot traversing an environment to learn representations of encountered objects. Knowledge of ego-motion and depth perception enables the agent to effectively associate multiple object proposals, which serve as training data for learning object representations from unlabelled images. We demonstrate the utility of this representation in two ways. First, we can automatically discover objects by pe"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1806.03370","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":"1806.03370","created_at":"2026-05-18T00:13:43.583204+00:00"},{"alias_kind":"arxiv_version","alias_value":"1806.03370v1","created_at":"2026-05-18T00:13:43.583204+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1806.03370","created_at":"2026-05-18T00:13:43.583204+00:00"},{"alias_kind":"pith_short_12","alias_value":"RRGNX7ILHDYW","created_at":"2026-05-18T12:32:50.500415+00:00"},{"alias_kind":"pith_short_16","alias_value":"RRGNX7ILHDYW23GE","created_at":"2026-05-18T12:32:50.500415+00:00"},{"alias_kind":"pith_short_8","alias_value":"RRGNX7IL","created_at":"2026-05-18T12:32:50.500415+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/RRGNX7ILHDYW23GE56KHD6WZGE","json":"https://pith.science/pith/RRGNX7ILHDYW23GE56KHD6WZGE.json","graph_json":"https://pith.science/api/pith-number/RRGNX7ILHDYW23GE56KHD6WZGE/graph.json","events_json":"https://pith.science/api/pith-number/RRGNX7ILHDYW23GE56KHD6WZGE/events.json","paper":"https://pith.science/paper/RRGNX7IL"},"agent_actions":{"view_html":"https://pith.science/pith/RRGNX7ILHDYW23GE56KHD6WZGE","download_json":"https://pith.science/pith/RRGNX7ILHDYW23GE56KHD6WZGE.json","view_paper":"https://pith.science/paper/RRGNX7IL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1806.03370&json=true","fetch_graph":"https://pith.science/api/pith-number/RRGNX7ILHDYW23GE56KHD6WZGE/graph.json","fetch_events":"https://pith.science/api/pith-number/RRGNX7ILHDYW23GE56KHD6WZGE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RRGNX7ILHDYW23GE56KHD6WZGE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RRGNX7ILHDYW23GE56KHD6WZGE/action/storage_attestation","attest_author":"https://pith.science/pith/RRGNX7ILHDYW23GE56KHD6WZGE/action/author_attestation","sign_citation":"https://pith.science/pith/RRGNX7ILHDYW23GE56KHD6WZGE/action/citation_signature","submit_replication":"https://pith.science/pith/RRGNX7ILHDYW23GE56KHD6WZGE/action/replication_record"}},"created_at":"2026-05-18T00:13:43.583204+00:00","updated_at":"2026-05-18T00:13:43.583204+00:00"}