{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2014:HAUFCSTFR2PPPS75ZFEGB33KYH","short_pith_number":"pith:HAUFCSTF","canonical_record":{"source":{"id":"1412.0623","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2014-12-01T20:11:44Z","cross_cats_sorted":[],"title_canon_sha256":"0b2311c59268f5925b8956fb957400e137c426d61489135a531f398e82208441","abstract_canon_sha256":"a317340441eb68d7b23509dc541f2e26c5cde171d3f2f18142f93862348dc992"},"schema_version":"1.0"},"canonical_sha256":"3828514a658e9ef7cbfdc94860ef6ac1f906a9e33c82c9f7089455f0dd9545ee","source":{"kind":"arxiv","id":"1412.0623","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1412.0623","created_at":"2026-05-18T02:18:58Z"},{"alias_kind":"arxiv_version","alias_value":"1412.0623v2","created_at":"2026-05-18T02:18:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1412.0623","created_at":"2026-05-18T02:18:58Z"},{"alias_kind":"pith_short_12","alias_value":"HAUFCSTFR2PP","created_at":"2026-05-18T12:28:30Z"},{"alias_kind":"pith_short_16","alias_value":"HAUFCSTFR2PPPS75","created_at":"2026-05-18T12:28:30Z"},{"alias_kind":"pith_short_8","alias_value":"HAUFCSTF","created_at":"2026-05-18T12:28:30Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2014:HAUFCSTFR2PPPS75ZFEGB33KYH","target":"record","payload":{"canonical_record":{"source":{"id":"1412.0623","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2014-12-01T20:11:44Z","cross_cats_sorted":[],"title_canon_sha256":"0b2311c59268f5925b8956fb957400e137c426d61489135a531f398e82208441","abstract_canon_sha256":"a317340441eb68d7b23509dc541f2e26c5cde171d3f2f18142f93862348dc992"},"schema_version":"1.0"},"canonical_sha256":"3828514a658e9ef7cbfdc94860ef6ac1f906a9e33c82c9f7089455f0dd9545ee","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:18:58.288199Z","signature_b64":"GekiEevl0mAvIHCHbVAnvC+i6Ybyh5VINAXOaNFvWHKGKtM8a715EMO4OmY2bsuPTcUzBIeEzIABtps9FPO+Bw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3828514a658e9ef7cbfdc94860ef6ac1f906a9e33c82c9f7089455f0dd9545ee","last_reissued_at":"2026-05-18T02:18:58.287537Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:18:58.287537Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1412.0623","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":"2026-05-18T02:18:58Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/fYcrh7jX06Moz/ViAz36ba4kGqn29eg/H4tJI1M3ofjq86NolV3hXUdsuQNIJwU84iYbzGIMeE9Enwmy5gtDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T01:38:22.026868Z"},"content_sha256":"02db83e1950a5587a77ccd518f5272c35904484b452f264acb3f390025c881d7","schema_version":"1.0","event_id":"sha256:02db83e1950a5587a77ccd518f5272c35904484b452f264acb3f390025c881d7"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2014:HAUFCSTFR2PPPS75ZFEGB33KYH","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Material Recognition in the Wild with the Materials in Context Database","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Kavita Bala, Noah Snavely, Paul Upchurch, Sean Bell","submitted_at":"2014-12-01T20:11:44Z","abstract_excerpt":"Recognizing materials in real-world images is a challenging task. Real-world materials have rich surface texture, geometry, lighting conditions, and clutter, which combine to make the problem particularly difficult. In this paper, we introduce a new, large-scale, open dataset of materials in the wild, the Materials in Context Database (MINC), and combine this dataset with deep learning to achieve material recognition and segmentation of images in the wild.\n  MINC is an order of magnitude larger than previous material databases, while being more diverse and well-sampled across its 23 categories"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1412.0623","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":"2026-05-18T02:18:58Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"UIzVocef+u0xfnuYGyG1L1voJXxeTa1bReH6O8yASzqNq1UKHlwienCfPrMWWcudiHYpKZerbZQsAENnY/B6Dg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T01:38:22.027569Z"},"content_sha256":"65f0b35cf2e7a3e4450b001b4d65c27bcb55e02da8d83dbcabac56aa6d33beae","schema_version":"1.0","event_id":"sha256:65f0b35cf2e7a3e4450b001b4d65c27bcb55e02da8d83dbcabac56aa6d33beae"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/HAUFCSTFR2PPPS75ZFEGB33KYH/bundle.json","state_url":"https://pith.science/pith/HAUFCSTFR2PPPS75ZFEGB33KYH/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/HAUFCSTFR2PPPS75ZFEGB33KYH/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":"2026-05-26T01:38:22Z","links":{"resolver":"https://pith.science/pith/HAUFCSTFR2PPPS75ZFEGB33KYH","bundle":"https://pith.science/pith/HAUFCSTFR2PPPS75ZFEGB33KYH/bundle.json","state":"https://pith.science/pith/HAUFCSTFR2PPPS75ZFEGB33KYH/state.json","well_known_bundle":"https://pith.science/.well-known/pith/HAUFCSTFR2PPPS75ZFEGB33KYH/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2014:HAUFCSTFR2PPPS75ZFEGB33KYH","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":"a317340441eb68d7b23509dc541f2e26c5cde171d3f2f18142f93862348dc992","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2014-12-01T20:11:44Z","title_canon_sha256":"0b2311c59268f5925b8956fb957400e137c426d61489135a531f398e82208441"},"schema_version":"1.0","source":{"id":"1412.0623","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1412.0623","created_at":"2026-05-18T02:18:58Z"},{"alias_kind":"arxiv_version","alias_value":"1412.0623v2","created_at":"2026-05-18T02:18:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1412.0623","created_at":"2026-05-18T02:18:58Z"},{"alias_kind":"pith_short_12","alias_value":"HAUFCSTFR2PP","created_at":"2026-05-18T12:28:30Z"},{"alias_kind":"pith_short_16","alias_value":"HAUFCSTFR2PPPS75","created_at":"2026-05-18T12:28:30Z"},{"alias_kind":"pith_short_8","alias_value":"HAUFCSTF","created_at":"2026-05-18T12:28:30Z"}],"graph_snapshots":[{"event_id":"sha256:65f0b35cf2e7a3e4450b001b4d65c27bcb55e02da8d83dbcabac56aa6d33beae","target":"graph","created_at":"2026-05-18T02:18:58Z","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":"Recognizing materials in real-world images is a challenging task. Real-world materials have rich surface texture, geometry, lighting conditions, and clutter, which combine to make the problem particularly difficult. In this paper, we introduce a new, large-scale, open dataset of materials in the wild, the Materials in Context Database (MINC), and combine this dataset with deep learning to achieve material recognition and segmentation of images in the wild.\n  MINC is an order of magnitude larger than previous material databases, while being more diverse and well-sampled across its 23 categories","authors_text":"Kavita Bala, Noah Snavely, Paul Upchurch, Sean Bell","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2014-12-01T20:11:44Z","title":"Material Recognition in the Wild with the Materials in Context Database"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1412.0623","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":"sha256:02db83e1950a5587a77ccd518f5272c35904484b452f264acb3f390025c881d7","target":"record","created_at":"2026-05-18T02:18:58Z","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":"a317340441eb68d7b23509dc541f2e26c5cde171d3f2f18142f93862348dc992","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2014-12-01T20:11:44Z","title_canon_sha256":"0b2311c59268f5925b8956fb957400e137c426d61489135a531f398e82208441"},"schema_version":"1.0","source":{"id":"1412.0623","kind":"arxiv","version":2}},"canonical_sha256":"3828514a658e9ef7cbfdc94860ef6ac1f906a9e33c82c9f7089455f0dd9545ee","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3828514a658e9ef7cbfdc94860ef6ac1f906a9e33c82c9f7089455f0dd9545ee","first_computed_at":"2026-05-18T02:18:58.287537Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T02:18:58.287537Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"GekiEevl0mAvIHCHbVAnvC+i6Ybyh5VINAXOaNFvWHKGKtM8a715EMO4OmY2bsuPTcUzBIeEzIABtps9FPO+Bw==","signature_status":"signed_v1","signed_at":"2026-05-18T02:18:58.288199Z","signed_message":"canonical_sha256_bytes"},"source_id":"1412.0623","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:02db83e1950a5587a77ccd518f5272c35904484b452f264acb3f390025c881d7","sha256:65f0b35cf2e7a3e4450b001b4d65c27bcb55e02da8d83dbcabac56aa6d33beae"],"state_sha256":"530c1cb75d2b382eb515920138be86199ae6a525a1090726706d5d878433b92a"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"hJN3Vy/tmSgqCiYFELbt5jeiuRtP542H41DKH5l7dwz1HGIWhpH3ioXGCc10pU9UFboh16RCUKK0NBx7qSLHCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-26T01:38:22.031349Z","bundle_sha256":"af5bc5a4b73883cc8d555b23a47efd4f600ccffec9a7bddb563eab0b15787e6c"}}