{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:G4AGEJK2BUQ3H3PJAKFVKJERPN","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":"89c1e01f529c068a144ff7e036b9d8fe22fbbf730bd227bd9026737ec2fef50a","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-01-04T11:55:55Z","title_canon_sha256":"ee9bf11c4d8427850a0f7593706b181bb433dc51f9aee355ddc92168c061aa17"},"schema_version":"1.0","source":{"id":"1801.01317","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1801.01317","created_at":"2026-05-18T00:26:42Z"},{"alias_kind":"arxiv_version","alias_value":"1801.01317v1","created_at":"2026-05-18T00:26:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1801.01317","created_at":"2026-05-18T00:26:42Z"},{"alias_kind":"pith_short_12","alias_value":"G4AGEJK2BUQ3","created_at":"2026-05-18T12:32:25Z"},{"alias_kind":"pith_short_16","alias_value":"G4AGEJK2BUQ3H3PJ","created_at":"2026-05-18T12:32:25Z"},{"alias_kind":"pith_short_8","alias_value":"G4AGEJK2","created_at":"2026-05-18T12:32:25Z"}],"graph_snapshots":[{"event_id":"sha256:b405a33af2b8a1ad2026e12f6e896c9be8ae3afb17edc20d2467892576407168","target":"graph","created_at":"2026-05-18T00:26:42Z","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 image segmentation is one of the most challenged tasks in computer vision. In this paper, we propose a highly fused convolutional network, which consists of three parts: feature downsampling, combined feature upsampling and multiple predictions. We adopt a strategy of multiple steps of upsampling and combined feature maps in pooling layers with its corresponding unpooling layers. Then we bring out multiple pre-outputs, each pre-output is generated from an unpooling layer by one-step upsampling. Finally, we concatenate these pre-outputs to get the final output. As a result, our propose","authors_text":"Junqiao Zhao, Linting Guan, Tao Yang, Yan Wu","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-01-04T11:55:55Z","title":"Semantic Segmentation via Highly Fused Convolutional Network with Multiple Soft Cost Functions"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1801.01317","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:ca51f73a49c99845d14dba1d31675850ff132d24b704a9dd9c5856d6880d390d","target":"record","created_at":"2026-05-18T00:26:42Z","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":"89c1e01f529c068a144ff7e036b9d8fe22fbbf730bd227bd9026737ec2fef50a","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-01-04T11:55:55Z","title_canon_sha256":"ee9bf11c4d8427850a0f7593706b181bb433dc51f9aee355ddc92168c061aa17"},"schema_version":"1.0","source":{"id":"1801.01317","kind":"arxiv","version":1}},"canonical_sha256":"370062255a0d21b3ede9028b5524917b69e90319e4e2fcfbd80e3ef3a0916e2a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"370062255a0d21b3ede9028b5524917b69e90319e4e2fcfbd80e3ef3a0916e2a","first_computed_at":"2026-05-18T00:26:42.466250Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:26:42.466250Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"rBD2dH1rAbgUoFlx8N2Q3EkmY3LI7y0iINvk9tE9ZCmxp++YvZBg0XQIT4yOwXu9eemGM4/HajwAppqK7r0yAQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:26:42.466849Z","signed_message":"canonical_sha256_bytes"},"source_id":"1801.01317","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ca51f73a49c99845d14dba1d31675850ff132d24b704a9dd9c5856d6880d390d","sha256:b405a33af2b8a1ad2026e12f6e896c9be8ae3afb17edc20d2467892576407168"],"state_sha256":"c92a0584e04e54259527d318dc433a3e007cc649b608f5f62095de072054b459"}