{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:EB2PGVU54LWAJS2X3DWHPWFOCY","short_pith_number":"pith:EB2PGVU5","canonical_record":{"source":{"id":"1706.10071","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-06-30T09:13:24Z","cross_cats_sorted":[],"title_canon_sha256":"071c3e56926cd6c4c114539e0533e76b14d962496e94ed46e87d9c118a56fa01","abstract_canon_sha256":"42d43142d23163a08943f222e0b9631e6560fe9887c4c68d1de0e5a767a8d76b"},"schema_version":"1.0"},"canonical_sha256":"2074f3569de2ec04cb57d8ec77d8ae161075b19d1496d73ae21d11b3be8f4716","source":{"kind":"arxiv","id":"1706.10071","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1706.10071","created_at":"2026-05-18T00:38:35Z"},{"alias_kind":"arxiv_version","alias_value":"1706.10071v2","created_at":"2026-05-18T00:38:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1706.10071","created_at":"2026-05-18T00:38:35Z"},{"alias_kind":"pith_short_12","alias_value":"EB2PGVU54LWA","created_at":"2026-05-18T12:31:12Z"},{"alias_kind":"pith_short_16","alias_value":"EB2PGVU54LWAJS2X","created_at":"2026-05-18T12:31:12Z"},{"alias_kind":"pith_short_8","alias_value":"EB2PGVU5","created_at":"2026-05-18T12:31:12Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:EB2PGVU54LWAJS2X3DWHPWFOCY","target":"record","payload":{"canonical_record":{"source":{"id":"1706.10071","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-06-30T09:13:24Z","cross_cats_sorted":[],"title_canon_sha256":"071c3e56926cd6c4c114539e0533e76b14d962496e94ed46e87d9c118a56fa01","abstract_canon_sha256":"42d43142d23163a08943f222e0b9631e6560fe9887c4c68d1de0e5a767a8d76b"},"schema_version":"1.0"},"canonical_sha256":"2074f3569de2ec04cb57d8ec77d8ae161075b19d1496d73ae21d11b3be8f4716","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:38:35.509682Z","signature_b64":"gMlVNZ+IuIDuVAnhRmfFtcu/omv/J+XsLIiIqd6UOSsDFoI3ecQzSzceeBfCiRlhWdgU5XG/xfhbEr0pdTYwAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2074f3569de2ec04cb57d8ec77d8ae161075b19d1496d73ae21d11b3be8f4716","last_reissued_at":"2026-05-18T00:38:35.509258Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:38:35.509258Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1706.10071","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":"2026-05-18T00:38:35Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"jZkqSCdqNHT7vxJjjAIUJ9EIuYU/B2icI5K+KXL4RRFJJxKC7shwAUYKo9pIS25qos+a1w3GQNbhIW4ozbCsDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T02:27:46.201597Z"},"content_sha256":"1234d16942509d5cb59cfd012d4daf6af34fbd53dbd6e5223f288a07078200b2","schema_version":"1.0","event_id":"sha256:1234d16942509d5cb59cfd012d4daf6af34fbd53dbd6e5223f288a07078200b2"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:EB2PGVU54LWAJS2X3DWHPWFOCY","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Superpixel-based Semantic Segmentation Trained by Statistical Process Control","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Hyojin Park, Jisoo Jeong, Nojun Kwak, Youngjoon Yoo","submitted_at":"2017-06-30T09:13:24Z","abstract_excerpt":"Semantic segmentation, like other fields of computer vision, has seen a remarkable performance advance by the use of deep convolution neural networks. However, considering that neighboring pixels are heavily dependent on each other, both learning and testing of these methods have a lot of redundant operations. To resolve this problem, the proposed network is trained and tested with only 0.37% of total pixels by superpixel-based sampling and largely reduced the complexity of upsampling calculation. The hypercolumn feature maps are constructed by pyramid module in combination with the convolutio"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1706.10071","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":"2026-05-18T00:38:35Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9n5zYv7WtOyLd+6Ca3NqF1TRdMVugAqi1O0JfybbVdAiZR3B3xyEyJLabDYRFgOvN7/XVWxa3i0NWq+7u/0PAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T02:27:46.202217Z"},"content_sha256":"7bbd5333623d024f29fe53677f3b3c0ae72ecaf04e297458be182ebf388d843b","schema_version":"1.0","event_id":"sha256:7bbd5333623d024f29fe53677f3b3c0ae72ecaf04e297458be182ebf388d843b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/EB2PGVU54LWAJS2X3DWHPWFOCY/bundle.json","state_url":"https://pith.science/pith/EB2PGVU54LWAJS2X3DWHPWFOCY/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/EB2PGVU54LWAJS2X3DWHPWFOCY/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":"2026-05-27T02:27:46Z","links":{"resolver":"https://pith.science/pith/EB2PGVU54LWAJS2X3DWHPWFOCY","bundle":"https://pith.science/pith/EB2PGVU54LWAJS2X3DWHPWFOCY/bundle.json","state":"https://pith.science/pith/EB2PGVU54LWAJS2X3DWHPWFOCY/state.json","well_known_bundle":"https://pith.science/.well-known/pith/EB2PGVU54LWAJS2X3DWHPWFOCY/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:EB2PGVU54LWAJS2X3DWHPWFOCY","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":"42d43142d23163a08943f222e0b9631e6560fe9887c4c68d1de0e5a767a8d76b","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-06-30T09:13:24Z","title_canon_sha256":"071c3e56926cd6c4c114539e0533e76b14d962496e94ed46e87d9c118a56fa01"},"schema_version":"1.0","source":{"id":"1706.10071","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1706.10071","created_at":"2026-05-18T00:38:35Z"},{"alias_kind":"arxiv_version","alias_value":"1706.10071v2","created_at":"2026-05-18T00:38:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1706.10071","created_at":"2026-05-18T00:38:35Z"},{"alias_kind":"pith_short_12","alias_value":"EB2PGVU54LWA","created_at":"2026-05-18T12:31:12Z"},{"alias_kind":"pith_short_16","alias_value":"EB2PGVU54LWAJS2X","created_at":"2026-05-18T12:31:12Z"},{"alias_kind":"pith_short_8","alias_value":"EB2PGVU5","created_at":"2026-05-18T12:31:12Z"}],"graph_snapshots":[{"event_id":"sha256:7bbd5333623d024f29fe53677f3b3c0ae72ecaf04e297458be182ebf388d843b","target":"graph","created_at":"2026-05-18T00:38:35Z","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":"Semantic segmentation, like other fields of computer vision, has seen a remarkable performance advance by the use of deep convolution neural networks. However, considering that neighboring pixels are heavily dependent on each other, both learning and testing of these methods have a lot of redundant operations. To resolve this problem, the proposed network is trained and tested with only 0.37% of total pixels by superpixel-based sampling and largely reduced the complexity of upsampling calculation. The hypercolumn feature maps are constructed by pyramid module in combination with the convolutio","authors_text":"Hyojin Park, Jisoo Jeong, Nojun Kwak, Youngjoon Yoo","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-06-30T09:13:24Z","title":"Superpixel-based Semantic Segmentation Trained by Statistical Process Control"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1706.10071","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":"sha256:1234d16942509d5cb59cfd012d4daf6af34fbd53dbd6e5223f288a07078200b2","target":"record","created_at":"2026-05-18T00:38:35Z","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":"42d43142d23163a08943f222e0b9631e6560fe9887c4c68d1de0e5a767a8d76b","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-06-30T09:13:24Z","title_canon_sha256":"071c3e56926cd6c4c114539e0533e76b14d962496e94ed46e87d9c118a56fa01"},"schema_version":"1.0","source":{"id":"1706.10071","kind":"arxiv","version":2}},"canonical_sha256":"2074f3569de2ec04cb57d8ec77d8ae161075b19d1496d73ae21d11b3be8f4716","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2074f3569de2ec04cb57d8ec77d8ae161075b19d1496d73ae21d11b3be8f4716","first_computed_at":"2026-05-18T00:38:35.509258Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:38:35.509258Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"gMlVNZ+IuIDuVAnhRmfFtcu/omv/J+XsLIiIqd6UOSsDFoI3ecQzSzceeBfCiRlhWdgU5XG/xfhbEr0pdTYwAA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:38:35.509682Z","signed_message":"canonical_sha256_bytes"},"source_id":"1706.10071","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1234d16942509d5cb59cfd012d4daf6af34fbd53dbd6e5223f288a07078200b2","sha256:7bbd5333623d024f29fe53677f3b3c0ae72ecaf04e297458be182ebf388d843b"],"state_sha256":"930c879a6afc977e9b6114e1a86116c67ba60ebd423d0b1d0c0805574e980091"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ecQN7RyqVLM+1vplvLXAlYvyQt+l00SeYXj/s5YVdQApFbUe+QY4Gu42sEgvt6v8SPnZNsqyT1EM3bMmRgI4BA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-27T02:27:46.205538Z","bundle_sha256":"6d3e3a1485cd360586e54b6979ba490b201f52ebdcab9a7c997f73ae596dce43"}}