{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:GMLX7CKJLHG7C7GSLVPEGHMMRY","short_pith_number":"pith:GMLX7CKJ","canonical_record":{"source":{"id":"1909.07074","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-09-16T09:10:10Z","cross_cats_sorted":[],"title_canon_sha256":"2d230b5b45f4b458007807165a54611626b78323df934ec4289499c48206c6ef","abstract_canon_sha256":"ab639b9faef26293de5a200697c7ad556ee535753e94c0615049b2c9f8c5be7d"},"schema_version":"1.0"},"canonical_sha256":"33177f894959cdf17cd25d5e431d8c8e169fc2f9aace5722bdeb09fa6ff10790","source":{"kind":"arxiv","id":"1909.07074","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1909.07074","created_at":"2026-07-05T00:04:53Z"},{"alias_kind":"arxiv_version","alias_value":"1909.07074v1","created_at":"2026-07-05T00:04:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1909.07074","created_at":"2026-07-05T00:04:53Z"},{"alias_kind":"pith_short_12","alias_value":"GMLX7CKJLHG7","created_at":"2026-07-05T00:04:53Z"},{"alias_kind":"pith_short_16","alias_value":"GMLX7CKJLHG7C7GS","created_at":"2026-07-05T00:04:53Z"},{"alias_kind":"pith_short_8","alias_value":"GMLX7CKJ","created_at":"2026-07-05T00:04:53Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:GMLX7CKJLHG7C7GSLVPEGHMMRY","target":"record","payload":{"canonical_record":{"source":{"id":"1909.07074","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-09-16T09:10:10Z","cross_cats_sorted":[],"title_canon_sha256":"2d230b5b45f4b458007807165a54611626b78323df934ec4289499c48206c6ef","abstract_canon_sha256":"ab639b9faef26293de5a200697c7ad556ee535753e94c0615049b2c9f8c5be7d"},"schema_version":"1.0"},"canonical_sha256":"33177f894959cdf17cd25d5e431d8c8e169fc2f9aace5722bdeb09fa6ff10790","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:04:53.008142Z","signature_b64":"k7NR7nwdKp0rcld06gcM6/kClaXTSCoWC8SjLtdFrciVAfieAHq5kC3wLFzfyPbcBA8ox42EiZa+hsZyg0QfDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"33177f894959cdf17cd25d5e431d8c8e169fc2f9aace5722bdeb09fa6ff10790","last_reissued_at":"2026-07-05T00:04:53.007785Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:04:53.007785Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1909.07074","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":"2026-07-05T00:04:53Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"XzDGdyR7C/XqlSznqiaCM/2sutJ3j5ynoLKrPE3aP/fnrD/bAGsP+y7b8sy5Z++SvE/cRlUv23kVnfxFdkq9BA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-21T03:52:36.043583Z"},"content_sha256":"968834bb0cbbbb124140478f97619571dfa5773d9a8348a480ec9f12dff61352","schema_version":"1.0","event_id":"sha256:968834bb0cbbbb124140478f97619571dfa5773d9a8348a480ec9f12dff61352"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:GMLX7CKJLHG7C7GSLVPEGHMMRY","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Temporally Consistent Depth Prediction with Flow-Guided Memory Units","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bumsub Ham, Chanho Eom, Hyunjong Park","submitted_at":"2019-09-16T09:10:10Z","abstract_excerpt":"Predicting depth from a monocular video sequence is an important task for autonomous driving. Although it has advanced considerably in the past few years, recent methods based on convolutional neural networks (CNNs) discard temporal coherence in the video sequence and estimate depth independently for each frame, which often leads to undesired inconsistent results over time. To address this problem, we propose to memorize temporal consistency in the video sequence, and leverage it for the task of depth prediction. To this end, we introduce a two-stream CNN with a flow-guided memory module, wher"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1909.07074","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/1909.07074/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2026-07-05T00:04:53Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"z/J2GNR8IFviOaU2YB3kBMQj8KFsBH1gPUnSP9tYW0fmieXw+9+Q/QuTmxm703FzL7Uew1DmQGNXX03NcVCYAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-21T03:52:36.044572Z"},"content_sha256":"f8fbe3b64e67a98a91534c7b110b138973b4ef7f520561d6f6161b65c9515497","schema_version":"1.0","event_id":"sha256:f8fbe3b64e67a98a91534c7b110b138973b4ef7f520561d6f6161b65c9515497"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/GMLX7CKJLHG7C7GSLVPEGHMMRY/bundle.json","state_url":"https://pith.science/pith/GMLX7CKJLHG7C7GSLVPEGHMMRY/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/GMLX7CKJLHG7C7GSLVPEGHMMRY/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":"2026-08-21T03:52:36Z","links":{"resolver":"https://pith.science/pith/GMLX7CKJLHG7C7GSLVPEGHMMRY","bundle":"https://pith.science/pith/GMLX7CKJLHG7C7GSLVPEGHMMRY/bundle.json","state":"https://pith.science/pith/GMLX7CKJLHG7C7GSLVPEGHMMRY/state.json","well_known_bundle":"https://pith.science/.well-known/pith/GMLX7CKJLHG7C7GSLVPEGHMMRY/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:GMLX7CKJLHG7C7GSLVPEGHMMRY","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":"ab639b9faef26293de5a200697c7ad556ee535753e94c0615049b2c9f8c5be7d","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-09-16T09:10:10Z","title_canon_sha256":"2d230b5b45f4b458007807165a54611626b78323df934ec4289499c48206c6ef"},"schema_version":"1.0","source":{"id":"1909.07074","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1909.07074","created_at":"2026-07-05T00:04:53Z"},{"alias_kind":"arxiv_version","alias_value":"1909.07074v1","created_at":"2026-07-05T00:04:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1909.07074","created_at":"2026-07-05T00:04:53Z"},{"alias_kind":"pith_short_12","alias_value":"GMLX7CKJLHG7","created_at":"2026-07-05T00:04:53Z"},{"alias_kind":"pith_short_16","alias_value":"GMLX7CKJLHG7C7GS","created_at":"2026-07-05T00:04:53Z"},{"alias_kind":"pith_short_8","alias_value":"GMLX7CKJ","created_at":"2026-07-05T00:04:53Z"}],"graph_snapshots":[{"event_id":"sha256:f8fbe3b64e67a98a91534c7b110b138973b4ef7f520561d6f6161b65c9515497","target":"graph","created_at":"2026-07-05T00:04:53Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/1909.07074/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Predicting depth from a monocular video sequence is an important task for autonomous driving. Although it has advanced considerably in the past few years, recent methods based on convolutional neural networks (CNNs) discard temporal coherence in the video sequence and estimate depth independently for each frame, which often leads to undesired inconsistent results over time. To address this problem, we propose to memorize temporal consistency in the video sequence, and leverage it for the task of depth prediction. To this end, we introduce a two-stream CNN with a flow-guided memory module, wher","authors_text":"Bumsub Ham, Chanho Eom, Hyunjong Park","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-09-16T09:10:10Z","title":"Temporally Consistent Depth Prediction with Flow-Guided Memory Units"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1909.07074","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":"sha256:968834bb0cbbbb124140478f97619571dfa5773d9a8348a480ec9f12dff61352","target":"record","created_at":"2026-07-05T00:04:53Z","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":"ab639b9faef26293de5a200697c7ad556ee535753e94c0615049b2c9f8c5be7d","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-09-16T09:10:10Z","title_canon_sha256":"2d230b5b45f4b458007807165a54611626b78323df934ec4289499c48206c6ef"},"schema_version":"1.0","source":{"id":"1909.07074","kind":"arxiv","version":1}},"canonical_sha256":"33177f894959cdf17cd25d5e431d8c8e169fc2f9aace5722bdeb09fa6ff10790","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"33177f894959cdf17cd25d5e431d8c8e169fc2f9aace5722bdeb09fa6ff10790","first_computed_at":"2026-07-05T00:04:53.007785Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:04:53.007785Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"k7NR7nwdKp0rcld06gcM6/kClaXTSCoWC8SjLtdFrciVAfieAHq5kC3wLFzfyPbcBA8ox42EiZa+hsZyg0QfDQ==","signature_status":"signed_v1","signed_at":"2026-07-05T00:04:53.008142Z","signed_message":"canonical_sha256_bytes"},"source_id":"1909.07074","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:968834bb0cbbbb124140478f97619571dfa5773d9a8348a480ec9f12dff61352","sha256:f8fbe3b64e67a98a91534c7b110b138973b4ef7f520561d6f6161b65c9515497"],"state_sha256":"769089f521c7c096cb31b6902023fae2dce9c630b1f096de0a63f32b7e9abeb4"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"gEMKN1No2RxyrvXvibDx4MrnymhNKuORYHIdM1ozKxIy2MdK1MlWMH2Bd7U5Cu5T24/dIy+ciPL1SkEQZ5NwBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-21T03:52:36.053959Z","bundle_sha256":"cf20c4b276540c4289d9cff542fd2e23968a8c853060b66e54a5091de62779ad"}}