{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:3WRVSV5HMK6JVZJMHYPQC6ZJEJ","short_pith_number":"pith:3WRVSV5H","canonical_record":{"source":{"id":"1804.05472","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-04-16T01:52:01Z","cross_cats_sorted":[],"title_canon_sha256":"46c4d88485976d3e24bd3e98a11f3796a8996ff46f6516a6fe8bff3c7af4b238","abstract_canon_sha256":"f45c7efcc233fc9025b841c9afd044519f23addad29dab71becc48c525a21fa9"},"schema_version":"1.0"},"canonical_sha256":"dda35957a762bc9ae52c3e1f017b29225b5987dbb9d91bb9c32fc45ee3775929","source":{"kind":"arxiv","id":"1804.05472","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1804.05472","created_at":"2026-05-18T00:18:28Z"},{"alias_kind":"arxiv_version","alias_value":"1804.05472v1","created_at":"2026-05-18T00:18:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1804.05472","created_at":"2026-05-18T00:18:28Z"},{"alias_kind":"pith_short_12","alias_value":"3WRVSV5HMK6J","created_at":"2026-05-18T12:32:05Z"},{"alias_kind":"pith_short_16","alias_value":"3WRVSV5HMK6JVZJM","created_at":"2026-05-18T12:32:05Z"},{"alias_kind":"pith_short_8","alias_value":"3WRVSV5H","created_at":"2026-05-18T12:32:05Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:3WRVSV5HMK6JVZJMHYPQC6ZJEJ","target":"record","payload":{"canonical_record":{"source":{"id":"1804.05472","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-04-16T01:52:01Z","cross_cats_sorted":[],"title_canon_sha256":"46c4d88485976d3e24bd3e98a11f3796a8996ff46f6516a6fe8bff3c7af4b238","abstract_canon_sha256":"f45c7efcc233fc9025b841c9afd044519f23addad29dab71becc48c525a21fa9"},"schema_version":"1.0"},"canonical_sha256":"dda35957a762bc9ae52c3e1f017b29225b5987dbb9d91bb9c32fc45ee3775929","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:18:28.049116Z","signature_b64":"6JzIDB/NiTvgDRk/7QsYzZNMlcPysbobx6NujO2DyIz6zR2kmSPHPek0OZ9Gn4I15L7t6xURn05oBwDnqRjVDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"dda35957a762bc9ae52c3e1f017b29225b5987dbb9d91bb9c32fc45ee3775929","last_reissued_at":"2026-05-18T00:18:28.048786Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:18:28.048786Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1804.05472","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":"2026-05-18T00:18:28Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"xJGtgYZAkr2MoIkxImBm8TSA1DJP9ILD42rQ7uCZeSbbFavW6xmbgZBKr24AyzRqw+DuwADF/d2871/G7NurBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-07T17:05:34.878842Z"},"content_sha256":"e6fcbb840495bbda2bdbb3e5d997e7d891e290c475100b61b15481f80c53cc18","schema_version":"1.0","event_id":"sha256:e6fcbb840495bbda2bdbb3e5d997e7d891e290c475100b61b15481f80c53cc18"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:3WRVSV5HMK6JVZJMHYPQC6ZJEJ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Optimizing Video Object Detection via a Scale-Time Lattice","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chen Change Loy, Dahua Lin, Jiaqi Wang, Kai Chen, Shuo Yang, Xingcheng Zhang, Yuanjun Xiong","submitted_at":"2018-04-16T01:52:01Z","abstract_excerpt":"High-performance object detection relies on expensive convolutional networks to compute features, often leading to significant challenges in applications, e.g. those that require detecting objects from video streams in real time. The key to this problem is to trade accuracy for efficiency in an effective way, i.e. reducing the computing cost while maintaining competitive performance. To seek a good balance, previous efforts usually focus on optimizing the model architectures. This paper explores an alternative approach, that is, to reallocate the computation over a scale-time space. The basic "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1804.05472","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":"2026-05-18T00:18:28Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"4iF+nuiVNYY0eP5zzUzNFH5xLYG6fIN4lTmZbTDDO+KtyBl9FdomLO6iUYrCt0M6lebmhfGk8YnyNFU0fWuUAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-07T17:05:34.879577Z"},"content_sha256":"3279bb6da5016b96eb97389822c6cbb4add160da3d62d2055290699087eae6ec","schema_version":"1.0","event_id":"sha256:3279bb6da5016b96eb97389822c6cbb4add160da3d62d2055290699087eae6ec"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/3WRVSV5HMK6JVZJMHYPQC6ZJEJ/bundle.json","state_url":"https://pith.science/pith/3WRVSV5HMK6JVZJMHYPQC6ZJEJ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/3WRVSV5HMK6JVZJMHYPQC6ZJEJ/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":"2026-06-07T17:05:34Z","links":{"resolver":"https://pith.science/pith/3WRVSV5HMK6JVZJMHYPQC6ZJEJ","bundle":"https://pith.science/pith/3WRVSV5HMK6JVZJMHYPQC6ZJEJ/bundle.json","state":"https://pith.science/pith/3WRVSV5HMK6JVZJMHYPQC6ZJEJ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/3WRVSV5HMK6JVZJMHYPQC6ZJEJ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:3WRVSV5HMK6JVZJMHYPQC6ZJEJ","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":"f45c7efcc233fc9025b841c9afd044519f23addad29dab71becc48c525a21fa9","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-04-16T01:52:01Z","title_canon_sha256":"46c4d88485976d3e24bd3e98a11f3796a8996ff46f6516a6fe8bff3c7af4b238"},"schema_version":"1.0","source":{"id":"1804.05472","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1804.05472","created_at":"2026-05-18T00:18:28Z"},{"alias_kind":"arxiv_version","alias_value":"1804.05472v1","created_at":"2026-05-18T00:18:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1804.05472","created_at":"2026-05-18T00:18:28Z"},{"alias_kind":"pith_short_12","alias_value":"3WRVSV5HMK6J","created_at":"2026-05-18T12:32:05Z"},{"alias_kind":"pith_short_16","alias_value":"3WRVSV5HMK6JVZJM","created_at":"2026-05-18T12:32:05Z"},{"alias_kind":"pith_short_8","alias_value":"3WRVSV5H","created_at":"2026-05-18T12:32:05Z"}],"graph_snapshots":[{"event_id":"sha256:3279bb6da5016b96eb97389822c6cbb4add160da3d62d2055290699087eae6ec","target":"graph","created_at":"2026-05-18T00:18:28Z","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":"High-performance object detection relies on expensive convolutional networks to compute features, often leading to significant challenges in applications, e.g. those that require detecting objects from video streams in real time. The key to this problem is to trade accuracy for efficiency in an effective way, i.e. reducing the computing cost while maintaining competitive performance. To seek a good balance, previous efforts usually focus on optimizing the model architectures. This paper explores an alternative approach, that is, to reallocate the computation over a scale-time space. The basic ","authors_text":"Chen Change Loy, Dahua Lin, Jiaqi Wang, Kai Chen, Shuo Yang, Xingcheng Zhang, Yuanjun Xiong","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-04-16T01:52:01Z","title":"Optimizing Video Object Detection via a Scale-Time Lattice"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1804.05472","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:e6fcbb840495bbda2bdbb3e5d997e7d891e290c475100b61b15481f80c53cc18","target":"record","created_at":"2026-05-18T00:18:28Z","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":"f45c7efcc233fc9025b841c9afd044519f23addad29dab71becc48c525a21fa9","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-04-16T01:52:01Z","title_canon_sha256":"46c4d88485976d3e24bd3e98a11f3796a8996ff46f6516a6fe8bff3c7af4b238"},"schema_version":"1.0","source":{"id":"1804.05472","kind":"arxiv","version":1}},"canonical_sha256":"dda35957a762bc9ae52c3e1f017b29225b5987dbb9d91bb9c32fc45ee3775929","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"dda35957a762bc9ae52c3e1f017b29225b5987dbb9d91bb9c32fc45ee3775929","first_computed_at":"2026-05-18T00:18:28.048786Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:18:28.048786Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"6JzIDB/NiTvgDRk/7QsYzZNMlcPysbobx6NujO2DyIz6zR2kmSPHPek0OZ9Gn4I15L7t6xURn05oBwDnqRjVDA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:18:28.049116Z","signed_message":"canonical_sha256_bytes"},"source_id":"1804.05472","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e6fcbb840495bbda2bdbb3e5d997e7d891e290c475100b61b15481f80c53cc18","sha256:3279bb6da5016b96eb97389822c6cbb4add160da3d62d2055290699087eae6ec"],"state_sha256":"1066bb6a184f2e1dbaeb242b43a399065adc437f6eebb5e7dc04d5dcdd3c92f3"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"sJC5x6tMTKY3lq3oxQkyMYgNGFLzTiYXIut0Isr9Rr4nvf3d/aU3rJsrmZZTr8Hroy++bEbnqmQ6CcyfKW4ABg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-07T17:05:34.883264Z","bundle_sha256":"a4f5fa8299b10741f22ac4542b803d18f65098a1a4e01b0b49cbd9c6beae8df6"}}