{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:URGX725INVZCP5CMRWDQDR3NDB","short_pith_number":"pith:URGX725I","canonical_record":{"source":{"id":"1810.12829","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-10-30T15:57:18Z","cross_cats_sorted":[],"title_canon_sha256":"3aeb931c9ab47a47a4f3da34c2a780ed20f7c4be595454b239dc1770ea3513f1","abstract_canon_sha256":"494d156bdd94c21e43adb0dddbd690e057b957a69ad06ecf3ad06730258a4b9c"},"schema_version":"1.0"},"canonical_sha256":"a44d7feba86d7227f44c8d8701c76d187fcd4405cca1f5d8741b45b3c646ee99","source":{"kind":"arxiv","id":"1810.12829","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1810.12829","created_at":"2026-05-17T23:57:37Z"},{"alias_kind":"arxiv_version","alias_value":"1810.12829v1","created_at":"2026-05-17T23:57:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1810.12829","created_at":"2026-05-17T23:57:37Z"},{"alias_kind":"pith_short_12","alias_value":"URGX725INVZC","created_at":"2026-05-18T12:32:56Z"},{"alias_kind":"pith_short_16","alias_value":"URGX725INVZCP5CM","created_at":"2026-05-18T12:32:56Z"},{"alias_kind":"pith_short_8","alias_value":"URGX725I","created_at":"2026-05-18T12:32:56Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:URGX725INVZCP5CMRWDQDR3NDB","target":"record","payload":{"canonical_record":{"source":{"id":"1810.12829","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-10-30T15:57:18Z","cross_cats_sorted":[],"title_canon_sha256":"3aeb931c9ab47a47a4f3da34c2a780ed20f7c4be595454b239dc1770ea3513f1","abstract_canon_sha256":"494d156bdd94c21e43adb0dddbd690e057b957a69ad06ecf3ad06730258a4b9c"},"schema_version":"1.0"},"canonical_sha256":"a44d7feba86d7227f44c8d8701c76d187fcd4405cca1f5d8741b45b3c646ee99","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:57:37.895171Z","signature_b64":"aEbq/dZpq+9vx1Up+jOw/71rxpXR/NkOyGN2XaUrkzwJYGnZs8MsjYKYDwzjvyl76zULkd4cLMAyvyEqIgnABg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a44d7feba86d7227f44c8d8701c76d187fcd4405cca1f5d8741b45b3c646ee99","last_reissued_at":"2026-05-17T23:57:37.894658Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:57:37.894658Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1810.12829","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":"2026-05-17T23:57:37Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2S3YT/4iKVs0Q0Rtws2ZQ9cJTb8b1bw6olZ5GSJ7uQ6pvqHrH/ZFu47FSK9B1qTDwSVdV/qtDrKu4n+oM6TvDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-08T22:14:58.815378Z"},"content_sha256":"6a2c852e378f7ae9cba287f700c3385cd0039ecadff54e966a7badbcc92e953c","schema_version":"1.0","event_id":"sha256:6a2c852e378f7ae9cba287f700c3385cd0039ecadff54e966a7badbcc92e953c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:URGX725INVZCP5CMRWDQDR3NDB","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Cross-Modal Attentional Context Learning for RGB-D Object Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Guanbin Li, Hejun Wu, Liang Lin, Nong Xiao, Yukang Gan","submitted_at":"2018-10-30T15:57:18Z","abstract_excerpt":"Recognizing objects from simultaneously sensed photometric (RGB) and depth channels is a fundamental yet practical problem in many machine vision applications such as robot grasping and autonomous driving. In this paper, we address this problem by developing a Cross-Modal Attentional Context (CMAC) learning framework, which enables the full exploitation of the context information from both RGB and depth data. Compared to existing RGB-D object detection frameworks, our approach has several appealing properties. First, it consists of an attention-based global context model for exploiting adaptiv"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.12829","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:57:37Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"HxmjKHCka2I6qfbtLc56kXqR3V4ytmhr7zwyFjPMJWXyk6Ynq9uSZg0Z8hxaHSSKx/oMqEJqnOquyTZStA5OBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-08T22:14:58.815724Z"},"content_sha256":"81abc488c227fb066e88d34bce724c2917cbb4068103092bb31e627d7682fa87","schema_version":"1.0","event_id":"sha256:81abc488c227fb066e88d34bce724c2917cbb4068103092bb31e627d7682fa87"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/URGX725INVZCP5CMRWDQDR3NDB/bundle.json","state_url":"https://pith.science/pith/URGX725INVZCP5CMRWDQDR3NDB/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/URGX725INVZCP5CMRWDQDR3NDB/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":"2026-06-08T22:14:58Z","links":{"resolver":"https://pith.science/pith/URGX725INVZCP5CMRWDQDR3NDB","bundle":"https://pith.science/pith/URGX725INVZCP5CMRWDQDR3NDB/bundle.json","state":"https://pith.science/pith/URGX725INVZCP5CMRWDQDR3NDB/state.json","well_known_bundle":"https://pith.science/.well-known/pith/URGX725INVZCP5CMRWDQDR3NDB/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:URGX725INVZCP5CMRWDQDR3NDB","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":"494d156bdd94c21e43adb0dddbd690e057b957a69ad06ecf3ad06730258a4b9c","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-10-30T15:57:18Z","title_canon_sha256":"3aeb931c9ab47a47a4f3da34c2a780ed20f7c4be595454b239dc1770ea3513f1"},"schema_version":"1.0","source":{"id":"1810.12829","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1810.12829","created_at":"2026-05-17T23:57:37Z"},{"alias_kind":"arxiv_version","alias_value":"1810.12829v1","created_at":"2026-05-17T23:57:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1810.12829","created_at":"2026-05-17T23:57:37Z"},{"alias_kind":"pith_short_12","alias_value":"URGX725INVZC","created_at":"2026-05-18T12:32:56Z"},{"alias_kind":"pith_short_16","alias_value":"URGX725INVZCP5CM","created_at":"2026-05-18T12:32:56Z"},{"alias_kind":"pith_short_8","alias_value":"URGX725I","created_at":"2026-05-18T12:32:56Z"}],"graph_snapshots":[{"event_id":"sha256:81abc488c227fb066e88d34bce724c2917cbb4068103092bb31e627d7682fa87","target":"graph","created_at":"2026-05-17T23:57:37Z","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"},"paper":{"abstract_excerpt":"Recognizing objects from simultaneously sensed photometric (RGB) and depth channels is a fundamental yet practical problem in many machine vision applications such as robot grasping and autonomous driving. In this paper, we address this problem by developing a Cross-Modal Attentional Context (CMAC) learning framework, which enables the full exploitation of the context information from both RGB and depth data. Compared to existing RGB-D object detection frameworks, our approach has several appealing properties. First, it consists of an attention-based global context model for exploiting adaptiv","authors_text":"Guanbin Li, Hejun Wu, Liang Lin, Nong Xiao, Yukang Gan","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-10-30T15:57:18Z","title":"Cross-Modal Attentional Context Learning for RGB-D Object Detection"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.12829","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":"sha256:6a2c852e378f7ae9cba287f700c3385cd0039ecadff54e966a7badbcc92e953c","target":"record","created_at":"2026-05-17T23:57:37Z","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":"494d156bdd94c21e43adb0dddbd690e057b957a69ad06ecf3ad06730258a4b9c","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-10-30T15:57:18Z","title_canon_sha256":"3aeb931c9ab47a47a4f3da34c2a780ed20f7c4be595454b239dc1770ea3513f1"},"schema_version":"1.0","source":{"id":"1810.12829","kind":"arxiv","version":1}},"canonical_sha256":"a44d7feba86d7227f44c8d8701c76d187fcd4405cca1f5d8741b45b3c646ee99","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a44d7feba86d7227f44c8d8701c76d187fcd4405cca1f5d8741b45b3c646ee99","first_computed_at":"2026-05-17T23:57:37.894658Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:57:37.894658Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"aEbq/dZpq+9vx1Up+jOw/71rxpXR/NkOyGN2XaUrkzwJYGnZs8MsjYKYDwzjvyl76zULkd4cLMAyvyEqIgnABg==","signature_status":"signed_v1","signed_at":"2026-05-17T23:57:37.895171Z","signed_message":"canonical_sha256_bytes"},"source_id":"1810.12829","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:6a2c852e378f7ae9cba287f700c3385cd0039ecadff54e966a7badbcc92e953c","sha256:81abc488c227fb066e88d34bce724c2917cbb4068103092bb31e627d7682fa87"],"state_sha256":"45b85dc9e0fc8de394bb3dc0e41919a0745a19f81e6853009ace4d1a0bd93724"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"GE7/IH+zgbMUpWYBy0VLBHiRciiuqcVP4YcAbhljw3Rea6KdHcNDKAv0nbGaF2m85vK4Fw6ePL9zFycbObkqBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-08T22:14:58.817558Z","bundle_sha256":"71a6f4187b7acec74e23568f2bb19e8e517b45b01f8fec3e2d2f67f68d856524"}}