{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:EH3BWHLBVAWXQRTQXXP3NGAH3G","short_pith_number":"pith:EH3BWHLB","canonical_record":{"source":{"id":"1808.06698","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-08-20T21:08:02Z","cross_cats_sorted":["cs.GR"],"title_canon_sha256":"21f5ccbd28a47d0f538f55e9b93ce8cce28b533caaf233245062499a52b36576","abstract_canon_sha256":"fdff6dc41cd03738bad5e44458438ac021296fd90cef87c5dace717f19626da7"},"schema_version":"1.0"},"canonical_sha256":"21f61b1d61a82d784670bddfb69807d9a2a0a1e2781a674ae59b94787b732308","source":{"kind":"arxiv","id":"1808.06698","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1808.06698","created_at":"2026-05-18T00:07:39Z"},{"alias_kind":"arxiv_version","alias_value":"1808.06698v1","created_at":"2026-05-18T00:07:39Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1808.06698","created_at":"2026-05-18T00:07:39Z"},{"alias_kind":"pith_short_12","alias_value":"EH3BWHLBVAWX","created_at":"2026-05-18T12:32:22Z"},{"alias_kind":"pith_short_16","alias_value":"EH3BWHLBVAWXQRTQ","created_at":"2026-05-18T12:32:22Z"},{"alias_kind":"pith_short_8","alias_value":"EH3BWHLB","created_at":"2026-05-18T12:32:22Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:EH3BWHLBVAWXQRTQXXP3NGAH3G","target":"record","payload":{"canonical_record":{"source":{"id":"1808.06698","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-08-20T21:08:02Z","cross_cats_sorted":["cs.GR"],"title_canon_sha256":"21f5ccbd28a47d0f538f55e9b93ce8cce28b533caaf233245062499a52b36576","abstract_canon_sha256":"fdff6dc41cd03738bad5e44458438ac021296fd90cef87c5dace717f19626da7"},"schema_version":"1.0"},"canonical_sha256":"21f61b1d61a82d784670bddfb69807d9a2a0a1e2781a674ae59b94787b732308","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:07:39.352908Z","signature_b64":"esDn8ML9RK8VguUv49L/o536OQRYioPuCsk4xVLN8eWKEodKhIA3vyVszgb2lgeO1314lTxQ4ee6KCDKR12wAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"21f61b1d61a82d784670bddfb69807d9a2a0a1e2781a674ae59b94787b732308","last_reissued_at":"2026-05-18T00:07:39.352188Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:07:39.352188Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1808.06698","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":"2026-05-18T00:07:39Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"eS2a0VOtEfHFMxb1ebxH2mvqACDFoQoBE4Wvy9TnBfDWEO9nbx9ShrEgh3olTtw71qwDBRAiHilMTIgXAfdqBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-03T09:05:06.629430Z"},"content_sha256":"c0595f57d1ca462e4da289a72967db5ad8ddfbfb933da28772193ff416840ec5","schema_version":"1.0","event_id":"sha256:c0595f57d1ca462e4da289a72967db5ad8ddfbfb933da28772193ff416840ec5"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:EH3BWHLBVAWXQRTQXXP3NGAH3G","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"VERAM: View-Enhanced Recurrent Attention Model for 3D Shape Classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.GR"],"primary_cat":"cs.CV","authors_text":"Kai Xu, Lintao Zheng, Songle Chen, Yan Zhang, ZhiXin Sun","submitted_at":"2018-08-20T21:08:02Z","abstract_excerpt":"Multi-view deep neural network is perhaps the most successful approach in 3D shape classification. However, the fusion of multi-view features based on max or average pooling lacks a view selection mechanism, limiting its application in, e.g., multi-view active object recognition by a robot. This paper presents VERAM, a recurrent attention model capable of actively selecting a sequence of views for highly accurate 3D shape classification. VERAM addresses an important issue commonly found in existing attention-based models, i.e., the unbalanced training of the subnetworks corresponding to next v"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1808.06698","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":"2026-05-18T00:07:39Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"rRgRz4vX2rVBjCD7255QMnyR1Hy+6W5BbwpM2hmzY54gF2lmGPs9tC9Pv3dXrH9+rVHVeQVajp5uYuN33t0sDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-03T09:05:06.629785Z"},"content_sha256":"614ca0d8776068eb22c1d3f04ac067c9c8282ed8a071b96a30474ccc3e772d87","schema_version":"1.0","event_id":"sha256:614ca0d8776068eb22c1d3f04ac067c9c8282ed8a071b96a30474ccc3e772d87"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/EH3BWHLBVAWXQRTQXXP3NGAH3G/bundle.json","state_url":"https://pith.science/pith/EH3BWHLBVAWXQRTQXXP3NGAH3G/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/EH3BWHLBVAWXQRTQXXP3NGAH3G/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":"2026-06-03T09:05:06Z","links":{"resolver":"https://pith.science/pith/EH3BWHLBVAWXQRTQXXP3NGAH3G","bundle":"https://pith.science/pith/EH3BWHLBVAWXQRTQXXP3NGAH3G/bundle.json","state":"https://pith.science/pith/EH3BWHLBVAWXQRTQXXP3NGAH3G/state.json","well_known_bundle":"https://pith.science/.well-known/pith/EH3BWHLBVAWXQRTQXXP3NGAH3G/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:EH3BWHLBVAWXQRTQXXP3NGAH3G","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":"fdff6dc41cd03738bad5e44458438ac021296fd90cef87c5dace717f19626da7","cross_cats_sorted":["cs.GR"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-08-20T21:08:02Z","title_canon_sha256":"21f5ccbd28a47d0f538f55e9b93ce8cce28b533caaf233245062499a52b36576"},"schema_version":"1.0","source":{"id":"1808.06698","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1808.06698","created_at":"2026-05-18T00:07:39Z"},{"alias_kind":"arxiv_version","alias_value":"1808.06698v1","created_at":"2026-05-18T00:07:39Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1808.06698","created_at":"2026-05-18T00:07:39Z"},{"alias_kind":"pith_short_12","alias_value":"EH3BWHLBVAWX","created_at":"2026-05-18T12:32:22Z"},{"alias_kind":"pith_short_16","alias_value":"EH3BWHLBVAWXQRTQ","created_at":"2026-05-18T12:32:22Z"},{"alias_kind":"pith_short_8","alias_value":"EH3BWHLB","created_at":"2026-05-18T12:32:22Z"}],"graph_snapshots":[{"event_id":"sha256:614ca0d8776068eb22c1d3f04ac067c9c8282ed8a071b96a30474ccc3e772d87","target":"graph","created_at":"2026-05-18T00:07:39Z","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":"Multi-view deep neural network is perhaps the most successful approach in 3D shape classification. However, the fusion of multi-view features based on max or average pooling lacks a view selection mechanism, limiting its application in, e.g., multi-view active object recognition by a robot. This paper presents VERAM, a recurrent attention model capable of actively selecting a sequence of views for highly accurate 3D shape classification. VERAM addresses an important issue commonly found in existing attention-based models, i.e., the unbalanced training of the subnetworks corresponding to next v","authors_text":"Kai Xu, Lintao Zheng, Songle Chen, Yan Zhang, ZhiXin Sun","cross_cats":["cs.GR"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-08-20T21:08:02Z","title":"VERAM: View-Enhanced Recurrent Attention Model for 3D Shape Classification"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1808.06698","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:c0595f57d1ca462e4da289a72967db5ad8ddfbfb933da28772193ff416840ec5","target":"record","created_at":"2026-05-18T00:07:39Z","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":"fdff6dc41cd03738bad5e44458438ac021296fd90cef87c5dace717f19626da7","cross_cats_sorted":["cs.GR"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-08-20T21:08:02Z","title_canon_sha256":"21f5ccbd28a47d0f538f55e9b93ce8cce28b533caaf233245062499a52b36576"},"schema_version":"1.0","source":{"id":"1808.06698","kind":"arxiv","version":1}},"canonical_sha256":"21f61b1d61a82d784670bddfb69807d9a2a0a1e2781a674ae59b94787b732308","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"21f61b1d61a82d784670bddfb69807d9a2a0a1e2781a674ae59b94787b732308","first_computed_at":"2026-05-18T00:07:39.352188Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:07:39.352188Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"esDn8ML9RK8VguUv49L/o536OQRYioPuCsk4xVLN8eWKEodKhIA3vyVszgb2lgeO1314lTxQ4ee6KCDKR12wAw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:07:39.352908Z","signed_message":"canonical_sha256_bytes"},"source_id":"1808.06698","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c0595f57d1ca462e4da289a72967db5ad8ddfbfb933da28772193ff416840ec5","sha256:614ca0d8776068eb22c1d3f04ac067c9c8282ed8a071b96a30474ccc3e772d87"],"state_sha256":"8de48a6860b4224119ebd7d0fbd4a255ae40510ad1ff81b2a1582b3be864a29b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"gKy0BXYuqjEKH8/Am+u1v9bXG6iC0zZk5zEicftbnp4CZJ/KHdBGLHUiOjf/nqavpEYSLQEHHL+2pWsVDpeoAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-03T09:05:06.631759Z","bundle_sha256":"e8d048fea8cbecb1ef2abc56107ad3dc5d20869887c4f3a43914d32118abd478"}}