{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:OO2GXZDJ6G23BXHWKFRHPUINLL","short_pith_number":"pith:OO2GXZDJ","canonical_record":{"source":{"id":"1811.11168","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-11-27T18:58:11Z","cross_cats_sorted":[],"title_canon_sha256":"c835d25e6a099743dd649449d0aeeb996ba59f10d9f649e584f38031c3eedcef","abstract_canon_sha256":"bbd3de7aa32b8b1dd710aeae97cb7fafe5e3fe613ae46f2df693ec88564abb72"},"schema_version":"1.0"},"canonical_sha256":"73b46be469f1b5b0dcf6516277d10d5ad92d1bb018027805816486c5df7451f9","source":{"kind":"arxiv","id":"1811.11168","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1811.11168","created_at":"2026-05-17T23:59:43Z"},{"alias_kind":"arxiv_version","alias_value":"1811.11168v2","created_at":"2026-05-17T23:59:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1811.11168","created_at":"2026-05-17T23:59:43Z"},{"alias_kind":"pith_short_12","alias_value":"OO2GXZDJ6G23","created_at":"2026-05-18T12:32:43Z"},{"alias_kind":"pith_short_16","alias_value":"OO2GXZDJ6G23BXHW","created_at":"2026-05-18T12:32:43Z"},{"alias_kind":"pith_short_8","alias_value":"OO2GXZDJ","created_at":"2026-05-18T12:32:43Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:OO2GXZDJ6G23BXHWKFRHPUINLL","target":"record","payload":{"canonical_record":{"source":{"id":"1811.11168","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-11-27T18:58:11Z","cross_cats_sorted":[],"title_canon_sha256":"c835d25e6a099743dd649449d0aeeb996ba59f10d9f649e584f38031c3eedcef","abstract_canon_sha256":"bbd3de7aa32b8b1dd710aeae97cb7fafe5e3fe613ae46f2df693ec88564abb72"},"schema_version":"1.0"},"canonical_sha256":"73b46be469f1b5b0dcf6516277d10d5ad92d1bb018027805816486c5df7451f9","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:59:43.183171Z","signature_b64":"J6HaFK3AkK0hIyJOhkMSmdgZ22H41NHTB3G63mctSG+UZvhGXns0KDLsiczATdHGSsl9gip/8JjzuCnlyNTBCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"73b46be469f1b5b0dcf6516277d10d5ad92d1bb018027805816486c5df7451f9","last_reissued_at":"2026-05-17T23:59:43.182547Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:59:43.182547Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1811.11168","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:59:43Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"IjSm0C6c2ejO1afR+6KdpyLBR5nI/Rtx9LptnOI8ot30X2dxRsAb15lBHSGYwocOyEMSpBkCMsm8Cr7+mb91BQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T23:43:18.399712Z"},"content_sha256":"b945592410b2040c08dd2caf07d891151f96fcda7954e7134bf8ed460e54db62","schema_version":"1.0","event_id":"sha256:b945592410b2040c08dd2caf07d891151f96fcda7954e7134bf8ed460e54db62"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:OO2GXZDJ6G23BXHWKFRHPUINLL","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Deformable ConvNets v2: More Deformable, Better Results","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Han Hu, Jifeng Dai, Stephen Lin, Xizhou Zhu","submitted_at":"2018-11-27T18:58:11Z","abstract_excerpt":"The superior performance of Deformable Convolutional Networks arises from its ability to adapt to the geometric variations of objects. Through an examination of its adaptive behavior, we observe that while the spatial support for its neural features conforms more closely than regular ConvNets to object structure, this support may nevertheless extend well beyond the region of interest, causing features to be influenced by irrelevant image content. To address this problem, we present a reformulation of Deformable ConvNets that improves its ability to focus on pertinent image regions, through inc"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1811.11168","kind":"arxiv","version":2},"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":"2026-05-17T23:59:43Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"KvPzk9rYvksaJyTSdF5MWxwWm0vkdIwHOFfPWEpjJkbTK4cUCGhBg5VUw1xuPbgJ46R4KDxw2uHzjcccnEmACg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T23:43:18.400358Z"},"content_sha256":"8f1f9ec7ea9c288f4e562f2cce6fd068db448ad103f2c5ad5399dbddcdadd4b4","schema_version":"1.0","event_id":"sha256:8f1f9ec7ea9c288f4e562f2cce6fd068db448ad103f2c5ad5399dbddcdadd4b4"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/OO2GXZDJ6G23BXHWKFRHPUINLL/bundle.json","state_url":"https://pith.science/pith/OO2GXZDJ6G23BXHWKFRHPUINLL/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/OO2GXZDJ6G23BXHWKFRHPUINLL/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":"2026-05-25T23:43:18Z","links":{"resolver":"https://pith.science/pith/OO2GXZDJ6G23BXHWKFRHPUINLL","bundle":"https://pith.science/pith/OO2GXZDJ6G23BXHWKFRHPUINLL/bundle.json","state":"https://pith.science/pith/OO2GXZDJ6G23BXHWKFRHPUINLL/state.json","well_known_bundle":"https://pith.science/.well-known/pith/OO2GXZDJ6G23BXHWKFRHPUINLL/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:OO2GXZDJ6G23BXHWKFRHPUINLL","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":"bbd3de7aa32b8b1dd710aeae97cb7fafe5e3fe613ae46f2df693ec88564abb72","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-11-27T18:58:11Z","title_canon_sha256":"c835d25e6a099743dd649449d0aeeb996ba59f10d9f649e584f38031c3eedcef"},"schema_version":"1.0","source":{"id":"1811.11168","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1811.11168","created_at":"2026-05-17T23:59:43Z"},{"alias_kind":"arxiv_version","alias_value":"1811.11168v2","created_at":"2026-05-17T23:59:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1811.11168","created_at":"2026-05-17T23:59:43Z"},{"alias_kind":"pith_short_12","alias_value":"OO2GXZDJ6G23","created_at":"2026-05-18T12:32:43Z"},{"alias_kind":"pith_short_16","alias_value":"OO2GXZDJ6G23BXHW","created_at":"2026-05-18T12:32:43Z"},{"alias_kind":"pith_short_8","alias_value":"OO2GXZDJ","created_at":"2026-05-18T12:32:43Z"}],"graph_snapshots":[{"event_id":"sha256:8f1f9ec7ea9c288f4e562f2cce6fd068db448ad103f2c5ad5399dbddcdadd4b4","target":"graph","created_at":"2026-05-17T23:59:43Z","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":"The superior performance of Deformable Convolutional Networks arises from its ability to adapt to the geometric variations of objects. Through an examination of its adaptive behavior, we observe that while the spatial support for its neural features conforms more closely than regular ConvNets to object structure, this support may nevertheless extend well beyond the region of interest, causing features to be influenced by irrelevant image content. To address this problem, we present a reformulation of Deformable ConvNets that improves its ability to focus on pertinent image regions, through inc","authors_text":"Han Hu, Jifeng Dai, Stephen Lin, Xizhou Zhu","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-11-27T18:58:11Z","title":"Deformable ConvNets v2: More Deformable, Better Results"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1811.11168","kind":"arxiv","version":2},"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:b945592410b2040c08dd2caf07d891151f96fcda7954e7134bf8ed460e54db62","target":"record","created_at":"2026-05-17T23:59:43Z","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":"bbd3de7aa32b8b1dd710aeae97cb7fafe5e3fe613ae46f2df693ec88564abb72","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-11-27T18:58:11Z","title_canon_sha256":"c835d25e6a099743dd649449d0aeeb996ba59f10d9f649e584f38031c3eedcef"},"schema_version":"1.0","source":{"id":"1811.11168","kind":"arxiv","version":2}},"canonical_sha256":"73b46be469f1b5b0dcf6516277d10d5ad92d1bb018027805816486c5df7451f9","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"73b46be469f1b5b0dcf6516277d10d5ad92d1bb018027805816486c5df7451f9","first_computed_at":"2026-05-17T23:59:43.182547Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:59:43.182547Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"J6HaFK3AkK0hIyJOhkMSmdgZ22H41NHTB3G63mctSG+UZvhGXns0KDLsiczATdHGSsl9gip/8JjzuCnlyNTBCA==","signature_status":"signed_v1","signed_at":"2026-05-17T23:59:43.183171Z","signed_message":"canonical_sha256_bytes"},"source_id":"1811.11168","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b945592410b2040c08dd2caf07d891151f96fcda7954e7134bf8ed460e54db62","sha256:8f1f9ec7ea9c288f4e562f2cce6fd068db448ad103f2c5ad5399dbddcdadd4b4"],"state_sha256":"8961a357d39d766b77e46a79503e4f3d7ce1adbbaf1a7d99361890c7de68f7d9"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"KsDLLTgGOjC+pYvA7LKQXexlw7PQ09ibja4EMUV/xyW8TBspVKGDUkOndf546xDVkzLIh3NxWNDeZvF48yXNDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-25T23:43:18.403793Z","bundle_sha256":"7444e5387c624c90841b2bd15fb051250dfa817fa9569b3a6ffa3945fa29c674"}}