{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2015:FTGUXP6PAHHVKNPD7KBD4UO75V","short_pith_number":"pith:FTGUXP6P","canonical_record":{"source":{"id":"1503.08843","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-03-30T20:17:23Z","cross_cats_sorted":[],"title_canon_sha256":"96024c2ebf85e39e768b87c23b3fb474eb9599f24b2d0b822db85d3ff671eacd","abstract_canon_sha256":"2661c90d844457a079cde307490ef0ae2e987e5877f8b3af16bc22e90053dde4"},"schema_version":"1.0"},"canonical_sha256":"2ccd4bbfcf01cf5535e3fa823e51dfed466683d50990c56151921db1d22915fa","source":{"kind":"arxiv","id":"1503.08843","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1503.08843","created_at":"2026-05-18T02:19:52Z"},{"alias_kind":"arxiv_version","alias_value":"1503.08843v1","created_at":"2026-05-18T02:19:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1503.08843","created_at":"2026-05-18T02:19:52Z"},{"alias_kind":"pith_short_12","alias_value":"FTGUXP6PAHHV","created_at":"2026-05-18T12:29:22Z"},{"alias_kind":"pith_short_16","alias_value":"FTGUXP6PAHHVKNPD","created_at":"2026-05-18T12:29:22Z"},{"alias_kind":"pith_short_8","alias_value":"FTGUXP6P","created_at":"2026-05-18T12:29:22Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2015:FTGUXP6PAHHVKNPD7KBD4UO75V","target":"record","payload":{"canonical_record":{"source":{"id":"1503.08843","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-03-30T20:17:23Z","cross_cats_sorted":[],"title_canon_sha256":"96024c2ebf85e39e768b87c23b3fb474eb9599f24b2d0b822db85d3ff671eacd","abstract_canon_sha256":"2661c90d844457a079cde307490ef0ae2e987e5877f8b3af16bc22e90053dde4"},"schema_version":"1.0"},"canonical_sha256":"2ccd4bbfcf01cf5535e3fa823e51dfed466683d50990c56151921db1d22915fa","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:19:52.089088Z","signature_b64":"WMrB1ijOXLvs/U2BaU7ifYJU0zVxRWP3YPBWT0wKZLnrzLXrrPWWpFTzas+nVdm/o7ge8RlDYe0j0r14Z+27AA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2ccd4bbfcf01cf5535e3fa823e51dfed466683d50990c56151921db1d22915fa","last_reissued_at":"2026-05-18T02:19:52.088401Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:19:52.088401Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1503.08843","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":"2026-05-18T02:19:52Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"kbYCGLJFWgvKoBTjegFZiSzgTH/P/Yriqymp4rdcJJAN8sw5rme2BeVVFOuGdPoJH5Ry4EdIYQxa2H2fvP80Cw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T23:32:57.508370Z"},"content_sha256":"dfe4cd9e0209ff3dd149b14c90a8895efaedde7af7a52631ec1f15aa43f433e7","schema_version":"1.0","event_id":"sha256:dfe4cd9e0209ff3dd149b14c90a8895efaedde7af7a52631ec1f15aa43f433e7"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2015:FTGUXP6PAHHVKNPD7KBD4UO75V","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Globally Tuned Cascade Pose Regression via Back Propagation with Application in 2D Face Pose Estimation and Heart Segmentation in 3D CT Images","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Guanglei Xiong, James K. Min, Peng Sun","submitted_at":"2015-03-30T20:17:23Z","abstract_excerpt":"Recently, a successful pose estimation algorithm, called Cascade Pose Regression (CPR), was proposed in the literature. Trained over Pose Index Feature, CPR is a regressor ensemble that is similar to Boosting. In this paper we show how CPR can be represented as a Neural Network. Specifically, we adopt a Graph Transformer Network (GTN) representation and accordingly train CPR with Back Propagation (BP) that permits globally tuning. In contrast, previous CPR literature only took a layer wise training without any post fine tuning. We empirically show that global training with BP outperforms layer"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1503.08843","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":"2026-05-18T02:19:52Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7lx+eUayL+k0zuGHUPxuvevap7hPLzI9Lim3tgska+7EF7r54yZrczZAXH82Hf+ZViZU1xMeOHC9hOAUje45AA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T23:32:57.508755Z"},"content_sha256":"efd5427cff65485041799ed43ab142d0663fd625be382197b105d5992eb5eff4","schema_version":"1.0","event_id":"sha256:efd5427cff65485041799ed43ab142d0663fd625be382197b105d5992eb5eff4"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/FTGUXP6PAHHVKNPD7KBD4UO75V/bundle.json","state_url":"https://pith.science/pith/FTGUXP6PAHHVKNPD7KBD4UO75V/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/FTGUXP6PAHHVKNPD7KBD4UO75V/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":"2026-05-31T23:32:57Z","links":{"resolver":"https://pith.science/pith/FTGUXP6PAHHVKNPD7KBD4UO75V","bundle":"https://pith.science/pith/FTGUXP6PAHHVKNPD7KBD4UO75V/bundle.json","state":"https://pith.science/pith/FTGUXP6PAHHVKNPD7KBD4UO75V/state.json","well_known_bundle":"https://pith.science/.well-known/pith/FTGUXP6PAHHVKNPD7KBD4UO75V/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2015:FTGUXP6PAHHVKNPD7KBD4UO75V","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":"2661c90d844457a079cde307490ef0ae2e987e5877f8b3af16bc22e90053dde4","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-03-30T20:17:23Z","title_canon_sha256":"96024c2ebf85e39e768b87c23b3fb474eb9599f24b2d0b822db85d3ff671eacd"},"schema_version":"1.0","source":{"id":"1503.08843","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1503.08843","created_at":"2026-05-18T02:19:52Z"},{"alias_kind":"arxiv_version","alias_value":"1503.08843v1","created_at":"2026-05-18T02:19:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1503.08843","created_at":"2026-05-18T02:19:52Z"},{"alias_kind":"pith_short_12","alias_value":"FTGUXP6PAHHV","created_at":"2026-05-18T12:29:22Z"},{"alias_kind":"pith_short_16","alias_value":"FTGUXP6PAHHVKNPD","created_at":"2026-05-18T12:29:22Z"},{"alias_kind":"pith_short_8","alias_value":"FTGUXP6P","created_at":"2026-05-18T12:29:22Z"}],"graph_snapshots":[{"event_id":"sha256:efd5427cff65485041799ed43ab142d0663fd625be382197b105d5992eb5eff4","target":"graph","created_at":"2026-05-18T02:19:52Z","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":"Recently, a successful pose estimation algorithm, called Cascade Pose Regression (CPR), was proposed in the literature. Trained over Pose Index Feature, CPR is a regressor ensemble that is similar to Boosting. In this paper we show how CPR can be represented as a Neural Network. Specifically, we adopt a Graph Transformer Network (GTN) representation and accordingly train CPR with Back Propagation (BP) that permits globally tuning. In contrast, previous CPR literature only took a layer wise training without any post fine tuning. We empirically show that global training with BP outperforms layer","authors_text":"Guanglei Xiong, James K. Min, Peng Sun","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-03-30T20:17:23Z","title":"Globally Tuned Cascade Pose Regression via Back Propagation with Application in 2D Face Pose Estimation and Heart Segmentation in 3D CT Images"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1503.08843","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:dfe4cd9e0209ff3dd149b14c90a8895efaedde7af7a52631ec1f15aa43f433e7","target":"record","created_at":"2026-05-18T02:19:52Z","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":"2661c90d844457a079cde307490ef0ae2e987e5877f8b3af16bc22e90053dde4","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-03-30T20:17:23Z","title_canon_sha256":"96024c2ebf85e39e768b87c23b3fb474eb9599f24b2d0b822db85d3ff671eacd"},"schema_version":"1.0","source":{"id":"1503.08843","kind":"arxiv","version":1}},"canonical_sha256":"2ccd4bbfcf01cf5535e3fa823e51dfed466683d50990c56151921db1d22915fa","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2ccd4bbfcf01cf5535e3fa823e51dfed466683d50990c56151921db1d22915fa","first_computed_at":"2026-05-18T02:19:52.088401Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T02:19:52.088401Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"WMrB1ijOXLvs/U2BaU7ifYJU0zVxRWP3YPBWT0wKZLnrzLXrrPWWpFTzas+nVdm/o7ge8RlDYe0j0r14Z+27AA==","signature_status":"signed_v1","signed_at":"2026-05-18T02:19:52.089088Z","signed_message":"canonical_sha256_bytes"},"source_id":"1503.08843","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:dfe4cd9e0209ff3dd149b14c90a8895efaedde7af7a52631ec1f15aa43f433e7","sha256:efd5427cff65485041799ed43ab142d0663fd625be382197b105d5992eb5eff4"],"state_sha256":"6fbf2ed2e0ae1f0ad6759b2214bb9d154fce0215b030d45e903ff15dbfd5ee86"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"xfYVz8/xy3uYirtTmxXgQQV8UWd/nV6F3WVCEbLiaXiG2ZwLMWBAKcbXGevug87k6NmgojvB+Hm5Winq1dXqCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-31T23:32:57.511499Z","bundle_sha256":"19fe392170569fc611d1811f049b34f630b4b7874700c245e8254c80ff425c25"}}