{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:SHGA4T3WCZ3QOOMRQ66PWD2UHF","short_pith_number":"pith:SHGA4T3W","canonical_record":{"source":{"id":"1805.04398","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-05-11T13:43:27Z","cross_cats_sorted":[],"title_canon_sha256":"93367e0b8ed540d1a40e64d8cf02fc63ee1ba41bb4c62db8ecd62ec91ba5e5a8","abstract_canon_sha256":"debe7566d5eb6bae0b7c2f256a20c2bb8d1834a38122e26074a7a1cb7974d579"},"schema_version":"1.0"},"canonical_sha256":"91cc0e4f76167707399187bcfb0f54397b348594bf7687caf3da9c5ae478c755","source":{"kind":"arxiv","id":"1805.04398","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1805.04398","created_at":"2026-05-18T00:16:10Z"},{"alias_kind":"arxiv_version","alias_value":"1805.04398v1","created_at":"2026-05-18T00:16:10Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.04398","created_at":"2026-05-18T00:16:10Z"},{"alias_kind":"pith_short_12","alias_value":"SHGA4T3WCZ3Q","created_at":"2026-05-18T12:32:53Z"},{"alias_kind":"pith_short_16","alias_value":"SHGA4T3WCZ3QOOMR","created_at":"2026-05-18T12:32:53Z"},{"alias_kind":"pith_short_8","alias_value":"SHGA4T3W","created_at":"2026-05-18T12:32:53Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:SHGA4T3WCZ3QOOMRQ66PWD2UHF","target":"record","payload":{"canonical_record":{"source":{"id":"1805.04398","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-05-11T13:43:27Z","cross_cats_sorted":[],"title_canon_sha256":"93367e0b8ed540d1a40e64d8cf02fc63ee1ba41bb4c62db8ecd62ec91ba5e5a8","abstract_canon_sha256":"debe7566d5eb6bae0b7c2f256a20c2bb8d1834a38122e26074a7a1cb7974d579"},"schema_version":"1.0"},"canonical_sha256":"91cc0e4f76167707399187bcfb0f54397b348594bf7687caf3da9c5ae478c755","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:16:10.448136Z","signature_b64":"NSwh/hBTmhjBit4yq6nmmcz8T7HQWZ+rwz0N04m+wHntJ3s+gkKMjr/7JUooKeLgw4d7FhXNmXyvVp255NhIBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"91cc0e4f76167707399187bcfb0f54397b348594bf7687caf3da9c5ae478c755","last_reissued_at":"2026-05-18T00:16:10.447482Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:16:10.447482Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1805.04398","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":"2026-05-18T00:16:10Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"rFBkp2Rkc//kYNh82zqgLBi6Un3aPEO/5KYOYtqWsuyIh2TXRZJvF1FujQUt/UZZn5ed2u728dusBnqqnHxXBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T19:38:39.666108Z"},"content_sha256":"aacb51dfafec85b916f057697ffc10d3784e1ce12c25848ede400a210eae2cca","schema_version":"1.0","event_id":"sha256:aacb51dfafec85b916f057697ffc10d3784e1ce12c25848ede400a210eae2cca"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:SHGA4T3WCZ3QOOMRQ66PWD2UHF","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Iteratively Trained Interactive Segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bastian Leibe, Paul Voigtlaender, Sabarinath Mahadevan","submitted_at":"2018-05-11T13:43:27Z","abstract_excerpt":"Deep learning requires large amounts of training data to be effective. For the task of object segmentation, manually labeling data is very expensive, and hence interactive methods are needed. Following recent approaches, we develop an interactive object segmentation system which uses user input in the form of clicks as the input to a convolutional network. While previous methods use heuristic click sampling strategies to emulate user clicks during training, we propose a new iterative training strategy. During training, we iteratively add clicks based on the errors of the currently predicted se"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.04398","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":"2026-05-18T00:16:10Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"zhfA1plgz02ScWIb2y8k+WqRAWMqH6oipy0LAiaHlxTnZ/RqUXokKDVxCIeZG9v49NDsRCYgEypwyk3kcxMsBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T19:38:39.666791Z"},"content_sha256":"7b8055881fdc5edebb8ebebe79b06052c00fdd7483baea6da3b70493978271d5","schema_version":"1.0","event_id":"sha256:7b8055881fdc5edebb8ebebe79b06052c00fdd7483baea6da3b70493978271d5"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/SHGA4T3WCZ3QOOMRQ66PWD2UHF/bundle.json","state_url":"https://pith.science/pith/SHGA4T3WCZ3QOOMRQ66PWD2UHF/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/SHGA4T3WCZ3QOOMRQ66PWD2UHF/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":"2026-05-25T19:38:39Z","links":{"resolver":"https://pith.science/pith/SHGA4T3WCZ3QOOMRQ66PWD2UHF","bundle":"https://pith.science/pith/SHGA4T3WCZ3QOOMRQ66PWD2UHF/bundle.json","state":"https://pith.science/pith/SHGA4T3WCZ3QOOMRQ66PWD2UHF/state.json","well_known_bundle":"https://pith.science/.well-known/pith/SHGA4T3WCZ3QOOMRQ66PWD2UHF/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:SHGA4T3WCZ3QOOMRQ66PWD2UHF","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":"debe7566d5eb6bae0b7c2f256a20c2bb8d1834a38122e26074a7a1cb7974d579","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-05-11T13:43:27Z","title_canon_sha256":"93367e0b8ed540d1a40e64d8cf02fc63ee1ba41bb4c62db8ecd62ec91ba5e5a8"},"schema_version":"1.0","source":{"id":"1805.04398","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1805.04398","created_at":"2026-05-18T00:16:10Z"},{"alias_kind":"arxiv_version","alias_value":"1805.04398v1","created_at":"2026-05-18T00:16:10Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.04398","created_at":"2026-05-18T00:16:10Z"},{"alias_kind":"pith_short_12","alias_value":"SHGA4T3WCZ3Q","created_at":"2026-05-18T12:32:53Z"},{"alias_kind":"pith_short_16","alias_value":"SHGA4T3WCZ3QOOMR","created_at":"2026-05-18T12:32:53Z"},{"alias_kind":"pith_short_8","alias_value":"SHGA4T3W","created_at":"2026-05-18T12:32:53Z"}],"graph_snapshots":[{"event_id":"sha256:7b8055881fdc5edebb8ebebe79b06052c00fdd7483baea6da3b70493978271d5","target":"graph","created_at":"2026-05-18T00:16:10Z","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":"Deep learning requires large amounts of training data to be effective. For the task of object segmentation, manually labeling data is very expensive, and hence interactive methods are needed. Following recent approaches, we develop an interactive object segmentation system which uses user input in the form of clicks as the input to a convolutional network. While previous methods use heuristic click sampling strategies to emulate user clicks during training, we propose a new iterative training strategy. During training, we iteratively add clicks based on the errors of the currently predicted se","authors_text":"Bastian Leibe, Paul Voigtlaender, Sabarinath Mahadevan","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-05-11T13:43:27Z","title":"Iteratively Trained Interactive Segmentation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.04398","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:aacb51dfafec85b916f057697ffc10d3784e1ce12c25848ede400a210eae2cca","target":"record","created_at":"2026-05-18T00:16:10Z","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":"debe7566d5eb6bae0b7c2f256a20c2bb8d1834a38122e26074a7a1cb7974d579","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-05-11T13:43:27Z","title_canon_sha256":"93367e0b8ed540d1a40e64d8cf02fc63ee1ba41bb4c62db8ecd62ec91ba5e5a8"},"schema_version":"1.0","source":{"id":"1805.04398","kind":"arxiv","version":1}},"canonical_sha256":"91cc0e4f76167707399187bcfb0f54397b348594bf7687caf3da9c5ae478c755","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"91cc0e4f76167707399187bcfb0f54397b348594bf7687caf3da9c5ae478c755","first_computed_at":"2026-05-18T00:16:10.447482Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:16:10.447482Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"NSwh/hBTmhjBit4yq6nmmcz8T7HQWZ+rwz0N04m+wHntJ3s+gkKMjr/7JUooKeLgw4d7FhXNmXyvVp255NhIBw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:16:10.448136Z","signed_message":"canonical_sha256_bytes"},"source_id":"1805.04398","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:aacb51dfafec85b916f057697ffc10d3784e1ce12c25848ede400a210eae2cca","sha256:7b8055881fdc5edebb8ebebe79b06052c00fdd7483baea6da3b70493978271d5"],"state_sha256":"302f5c9d1f66884a32a459335db9fd1e0d5e9d6c34bde4b319405fea1c0d403c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"qHfiw5H46qwWKbF51xGlfM4LqYv08TxgUmKZk5eyxswwfOhDemH4Zb+/CR7qXQbVlpA2876mKE1gDTlZ3bC6CQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-25T19:38:39.670633Z","bundle_sha256":"04ef7d30b9402d162e3be8d8fff2f290ab228949807529afaae6398a4a890008"}}