{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:QK5PRTMULWFNMCIUUJNGBBXMF6","short_pith_number":"pith:QK5PRTMU","canonical_record":{"source":{"id":"2203.01449","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-03-02T22:49:17Z","cross_cats_sorted":["cs.RO"],"title_canon_sha256":"1e18f0359559b47c6189ce1d21c2fea5efd7bc784da69098312af736d5db16c9","abstract_canon_sha256":"00665a01ff394eb51729984f0bf8adb7528a8389d9480e7d6d680e82d7309912"},"schema_version":"1.0"},"canonical_sha256":"82baf8cd945d8ad60914a25a6086ec2f8f23deea08bd8daf6032089fb0744e70","source":{"kind":"arxiv","id":"2203.01449","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2203.01449","created_at":"2026-07-05T04:01:48Z"},{"alias_kind":"arxiv_version","alias_value":"2203.01449v1","created_at":"2026-07-05T04:01:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2203.01449","created_at":"2026-07-05T04:01:48Z"},{"alias_kind":"pith_short_12","alias_value":"QK5PRTMULWFN","created_at":"2026-07-05T04:01:48Z"},{"alias_kind":"pith_short_16","alias_value":"QK5PRTMULWFNMCIU","created_at":"2026-07-05T04:01:48Z"},{"alias_kind":"pith_short_8","alias_value":"QK5PRTMU","created_at":"2026-07-05T04:01:48Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:QK5PRTMULWFNMCIUUJNGBBXMF6","target":"record","payload":{"canonical_record":{"source":{"id":"2203.01449","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-03-02T22:49:17Z","cross_cats_sorted":["cs.RO"],"title_canon_sha256":"1e18f0359559b47c6189ce1d21c2fea5efd7bc784da69098312af736d5db16c9","abstract_canon_sha256":"00665a01ff394eb51729984f0bf8adb7528a8389d9480e7d6d680e82d7309912"},"schema_version":"1.0"},"canonical_sha256":"82baf8cd945d8ad60914a25a6086ec2f8f23deea08bd8daf6032089fb0744e70","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:01:48.159233Z","signature_b64":"UvWl5UKbbY8iWowmMlOxp6RmMg2lNCTc41BKOFF3EWd06bVuuKwZlfSNt0pyJ4lcd4Tgf438g6WzF3HOUmhGBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"82baf8cd945d8ad60914a25a6086ec2f8f23deea08bd8daf6032089fb0744e70","last_reissued_at":"2026-07-05T04:01:48.158748Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:01:48.158748Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2203.01449","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T04:01:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"hbrBTu8ZM5NsWltWwBzavEqsQt0LB7Ciufe19XsMBd6auuHN4o9JnOYazqgbIsrxsAffNO1MoFfJh9TXWR2xBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-20T14:58:28.805319Z"},"content_sha256":"8e048ed9fb345a6f0d58c200c33ba3bce571527332fd32d197ce63c35f65f022","schema_version":"1.0","event_id":"sha256:8e048ed9fb345a6f0d58c200c33ba3bce571527332fd32d197ce63c35f65f022"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:QK5PRTMULWFNMCIUUJNGBBXMF6","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Object Pose Estimation using Mid-level Visual Representations","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.RO"],"primary_cat":"cs.CV","authors_text":"Jana Kosecka, Negar Nejatishahidin, Pooya Fayyazsanavi","submitted_at":"2022-03-02T22:49:17Z","abstract_excerpt":"This work proposes a novel pose estimation model for object categories that can be effectively transferred to previously unseen environments. The deep convolutional network models (CNN) for pose estimation are typically trained and evaluated on datasets specifically curated for object detection, pose estimation, or 3D reconstruction, which requires large amounts of training data. In this work, we propose a model for pose estimation that can be trained with small amount of data and is built on the top of generic mid-level representations \\cite{taskonomy2018} (e.g. surface normal estimation and "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2203.01449","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/2203.01449/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T04:01:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"vwM9KTA/udPJI4p4eve8kUKvrgs2YPMkWIqrqVcmagDxAmOc9suppFD8ZT27xeOH37GYRuJWohHKbqaB7/n3Ag==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-20T14:58:28.805712Z"},"content_sha256":"9035a07497a2658abc3094f0e62ab723c6096b726f7a50438248de197261d166","schema_version":"1.0","event_id":"sha256:9035a07497a2658abc3094f0e62ab723c6096b726f7a50438248de197261d166"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/QK5PRTMULWFNMCIUUJNGBBXMF6/bundle.json","state_url":"https://pith.science/pith/QK5PRTMULWFNMCIUUJNGBBXMF6/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/QK5PRTMULWFNMCIUUJNGBBXMF6/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-20T14:58:28Z","links":{"resolver":"https://pith.science/pith/QK5PRTMULWFNMCIUUJNGBBXMF6","bundle":"https://pith.science/pith/QK5PRTMULWFNMCIUUJNGBBXMF6/bundle.json","state":"https://pith.science/pith/QK5PRTMULWFNMCIUUJNGBBXMF6/state.json","well_known_bundle":"https://pith.science/.well-known/pith/QK5PRTMULWFNMCIUUJNGBBXMF6/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:QK5PRTMULWFNMCIUUJNGBBXMF6","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"00665a01ff394eb51729984f0bf8adb7528a8389d9480e7d6d680e82d7309912","cross_cats_sorted":["cs.RO"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-03-02T22:49:17Z","title_canon_sha256":"1e18f0359559b47c6189ce1d21c2fea5efd7bc784da69098312af736d5db16c9"},"schema_version":"1.0","source":{"id":"2203.01449","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2203.01449","created_at":"2026-07-05T04:01:48Z"},{"alias_kind":"arxiv_version","alias_value":"2203.01449v1","created_at":"2026-07-05T04:01:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2203.01449","created_at":"2026-07-05T04:01:48Z"},{"alias_kind":"pith_short_12","alias_value":"QK5PRTMULWFN","created_at":"2026-07-05T04:01:48Z"},{"alias_kind":"pith_short_16","alias_value":"QK5PRTMULWFNMCIU","created_at":"2026-07-05T04:01:48Z"},{"alias_kind":"pith_short_8","alias_value":"QK5PRTMU","created_at":"2026-07-05T04:01:48Z"}],"graph_snapshots":[{"event_id":"sha256:9035a07497a2658abc3094f0e62ab723c6096b726f7a50438248de197261d166","target":"graph","created_at":"2026-07-05T04:01:48Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2203.01449/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"This work proposes a novel pose estimation model for object categories that can be effectively transferred to previously unseen environments. The deep convolutional network models (CNN) for pose estimation are typically trained and evaluated on datasets specifically curated for object detection, pose estimation, or 3D reconstruction, which requires large amounts of training data. In this work, we propose a model for pose estimation that can be trained with small amount of data and is built on the top of generic mid-level representations \\cite{taskonomy2018} (e.g. surface normal estimation and ","authors_text":"Jana Kosecka, Negar Nejatishahidin, Pooya Fayyazsanavi","cross_cats":["cs.RO"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-03-02T22:49:17Z","title":"Object Pose Estimation using Mid-level Visual Representations"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2203.01449","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:8e048ed9fb345a6f0d58c200c33ba3bce571527332fd32d197ce63c35f65f022","target":"record","created_at":"2026-07-05T04:01:48Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"00665a01ff394eb51729984f0bf8adb7528a8389d9480e7d6d680e82d7309912","cross_cats_sorted":["cs.RO"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-03-02T22:49:17Z","title_canon_sha256":"1e18f0359559b47c6189ce1d21c2fea5efd7bc784da69098312af736d5db16c9"},"schema_version":"1.0","source":{"id":"2203.01449","kind":"arxiv","version":1}},"canonical_sha256":"82baf8cd945d8ad60914a25a6086ec2f8f23deea08bd8daf6032089fb0744e70","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"82baf8cd945d8ad60914a25a6086ec2f8f23deea08bd8daf6032089fb0744e70","first_computed_at":"2026-07-05T04:01:48.158748Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:01:48.158748Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"UvWl5UKbbY8iWowmMlOxp6RmMg2lNCTc41BKOFF3EWd06bVuuKwZlfSNt0pyJ4lcd4Tgf438g6WzF3HOUmhGBA==","signature_status":"signed_v1","signed_at":"2026-07-05T04:01:48.159233Z","signed_message":"canonical_sha256_bytes"},"source_id":"2203.01449","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:8e048ed9fb345a6f0d58c200c33ba3bce571527332fd32d197ce63c35f65f022","sha256:9035a07497a2658abc3094f0e62ab723c6096b726f7a50438248de197261d166"],"state_sha256":"89ef5b7741df78841518c3ca7f1cf4b7ef14032fff62ff485ebd22548c0d3c71"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"EMVaJLWD7pRv/ybTlfoqqCtBIq394o1wCwlYoVlHnKBN8kZrWxA3a1o/TxT3UMXXMot1w4HWss0l6A50mONaDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-20T14:58:28.808023Z","bundle_sha256":"cd26b78620de9b4e3b05624de7a31c5f1da17b8257b705fa930b4b991d71c5ae"}}