{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:4MHDNGMCYLOD32L5GCMCDKGKCT","short_pith_number":"pith:4MHDNGMC","canonical_record":{"source":{"id":"1606.01621","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-06-06T06:14:00Z","cross_cats_sorted":["cs.IR","cs.MM"],"title_canon_sha256":"2044f3afe0403e595450ed19cad62f021524d94fa61aea3a1f8d8e3b0c5c9f57","abstract_canon_sha256":"25135a81b151109218a84e120e7313414d541b9c530138c78c1f737198a78024"},"schema_version":"1.0"},"canonical_sha256":"e30e369982c2dc3de97d309821a8ca14d887486951a8901595f237d3380996c0","source":{"kind":"arxiv","id":"1606.01621","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1606.01621","created_at":"2026-05-18T01:10:23Z"},{"alias_kind":"arxiv_version","alias_value":"1606.01621v2","created_at":"2026-05-18T01:10:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1606.01621","created_at":"2026-05-18T01:10:23Z"},{"alias_kind":"pith_short_12","alias_value":"4MHDNGMCYLOD","created_at":"2026-05-18T12:29:58Z"},{"alias_kind":"pith_short_16","alias_value":"4MHDNGMCYLOD32L5","created_at":"2026-05-18T12:29:58Z"},{"alias_kind":"pith_short_8","alias_value":"4MHDNGMC","created_at":"2026-05-18T12:29:58Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:4MHDNGMCYLOD32L5GCMCDKGKCT","target":"record","payload":{"canonical_record":{"source":{"id":"1606.01621","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-06-06T06:14:00Z","cross_cats_sorted":["cs.IR","cs.MM"],"title_canon_sha256":"2044f3afe0403e595450ed19cad62f021524d94fa61aea3a1f8d8e3b0c5c9f57","abstract_canon_sha256":"25135a81b151109218a84e120e7313414d541b9c530138c78c1f737198a78024"},"schema_version":"1.0"},"canonical_sha256":"e30e369982c2dc3de97d309821a8ca14d887486951a8901595f237d3380996c0","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:10:23.199995Z","signature_b64":"SdzUOViqPRlAeJwh3JB+w9r+M9VOahSsOJyzoObH2MGA0H0JM8rfTDp+Etx9dZlMY1xqMyEI9zZyfTkDh3IcDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e30e369982c2dc3de97d309821a8ca14d887486951a8901595f237d3380996c0","last_reissued_at":"2026-05-18T01:10:23.199355Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:10:23.199355Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1606.01621","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":"2026-05-18T01:10:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"KgfEsSPDPQd0Tws5JQ9/TWh+jIuaUU3coHZASzM6YRrZNcAApPmTwdsvsOAV4dFo6Mu+xfph6nt2EvhvBPG/BQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-29T19:29:00.923204Z"},"content_sha256":"67a2898840af8fb9cf848cb7447e871fa269f964f918b8ac2f5753834af77995","schema_version":"1.0","event_id":"sha256:67a2898840af8fb9cf848cb7447e871fa269f964f918b8ac2f5753834af77995"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:4MHDNGMCYLOD32L5GCMCDKGKCT","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Photo Aesthetics Ranking Network with Attributes and Content Adaptation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IR","cs.MM"],"primary_cat":"cs.CV","authors_text":"Charless Fowlkes, Radomir Mech, Shu Kong, Xiaohui Shen, Zhe Lin","submitted_at":"2016-06-06T06:14:00Z","abstract_excerpt":"Real-world applications could benefit from the ability to automatically generate a fine-grained ranking of photo aesthetics. However, previous methods for image aesthetics analysis have primarily focused on the coarse, binary categorization of images into high- or low-aesthetic categories. In this work, we propose to learn a deep convolutional neural network to rank photo aesthetics in which the relative ranking of photo aesthetics are directly modeled in the loss function. Our model incorporates joint learning of meaningful photographic attributes and image content information which can help "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1606.01621","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":"2026-05-18T01:10:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"QZQlVmnZF/PGcHS+TT3JWFP1zYtj6y3GSvOPYA1tA3W/ImAK1THhAJeXXrbU8c6VuNDGleYWtcSVTh64yDrQDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-29T19:29:00.923570Z"},"content_sha256":"0fc30f3b97379d0cdab912466776f7a733af523068cc4a4abcc6653aef491634","schema_version":"1.0","event_id":"sha256:0fc30f3b97379d0cdab912466776f7a733af523068cc4a4abcc6653aef491634"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/4MHDNGMCYLOD32L5GCMCDKGKCT/bundle.json","state_url":"https://pith.science/pith/4MHDNGMCYLOD32L5GCMCDKGKCT/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/4MHDNGMCYLOD32L5GCMCDKGKCT/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":"2026-06-29T19:29:00Z","links":{"resolver":"https://pith.science/pith/4MHDNGMCYLOD32L5GCMCDKGKCT","bundle":"https://pith.science/pith/4MHDNGMCYLOD32L5GCMCDKGKCT/bundle.json","state":"https://pith.science/pith/4MHDNGMCYLOD32L5GCMCDKGKCT/state.json","well_known_bundle":"https://pith.science/.well-known/pith/4MHDNGMCYLOD32L5GCMCDKGKCT/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:4MHDNGMCYLOD32L5GCMCDKGKCT","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":"25135a81b151109218a84e120e7313414d541b9c530138c78c1f737198a78024","cross_cats_sorted":["cs.IR","cs.MM"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-06-06T06:14:00Z","title_canon_sha256":"2044f3afe0403e595450ed19cad62f021524d94fa61aea3a1f8d8e3b0c5c9f57"},"schema_version":"1.0","source":{"id":"1606.01621","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1606.01621","created_at":"2026-05-18T01:10:23Z"},{"alias_kind":"arxiv_version","alias_value":"1606.01621v2","created_at":"2026-05-18T01:10:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1606.01621","created_at":"2026-05-18T01:10:23Z"},{"alias_kind":"pith_short_12","alias_value":"4MHDNGMCYLOD","created_at":"2026-05-18T12:29:58Z"},{"alias_kind":"pith_short_16","alias_value":"4MHDNGMCYLOD32L5","created_at":"2026-05-18T12:29:58Z"},{"alias_kind":"pith_short_8","alias_value":"4MHDNGMC","created_at":"2026-05-18T12:29:58Z"}],"graph_snapshots":[{"event_id":"sha256:0fc30f3b97379d0cdab912466776f7a733af523068cc4a4abcc6653aef491634","target":"graph","created_at":"2026-05-18T01:10:23Z","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":"Real-world applications could benefit from the ability to automatically generate a fine-grained ranking of photo aesthetics. However, previous methods for image aesthetics analysis have primarily focused on the coarse, binary categorization of images into high- or low-aesthetic categories. In this work, we propose to learn a deep convolutional neural network to rank photo aesthetics in which the relative ranking of photo aesthetics are directly modeled in the loss function. Our model incorporates joint learning of meaningful photographic attributes and image content information which can help ","authors_text":"Charless Fowlkes, Radomir Mech, Shu Kong, Xiaohui Shen, Zhe Lin","cross_cats":["cs.IR","cs.MM"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-06-06T06:14:00Z","title":"Photo Aesthetics Ranking Network with Attributes and Content Adaptation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1606.01621","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":"sha256:67a2898840af8fb9cf848cb7447e871fa269f964f918b8ac2f5753834af77995","target":"record","created_at":"2026-05-18T01:10:23Z","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":"25135a81b151109218a84e120e7313414d541b9c530138c78c1f737198a78024","cross_cats_sorted":["cs.IR","cs.MM"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-06-06T06:14:00Z","title_canon_sha256":"2044f3afe0403e595450ed19cad62f021524d94fa61aea3a1f8d8e3b0c5c9f57"},"schema_version":"1.0","source":{"id":"1606.01621","kind":"arxiv","version":2}},"canonical_sha256":"e30e369982c2dc3de97d309821a8ca14d887486951a8901595f237d3380996c0","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e30e369982c2dc3de97d309821a8ca14d887486951a8901595f237d3380996c0","first_computed_at":"2026-05-18T01:10:23.199355Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:10:23.199355Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"SdzUOViqPRlAeJwh3JB+w9r+M9VOahSsOJyzoObH2MGA0H0JM8rfTDp+Etx9dZlMY1xqMyEI9zZyfTkDh3IcDg==","signature_status":"signed_v1","signed_at":"2026-05-18T01:10:23.199995Z","signed_message":"canonical_sha256_bytes"},"source_id":"1606.01621","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:67a2898840af8fb9cf848cb7447e871fa269f964f918b8ac2f5753834af77995","sha256:0fc30f3b97379d0cdab912466776f7a733af523068cc4a4abcc6653aef491634"],"state_sha256":"a236e03c00425d07da7a7c1495bcf72b5893f12d280dcbeaa40ca7d22526b75f"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Ff4t9Uoja3bbr5a32koIXNr00HUG+ilP4wl6nJyKIxqfyp8zGtMj5uegKZvCjw8xOreAvqSImpH5QiWZdyS6CQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-29T19:29:00.925479Z","bundle_sha256":"768894da8b3beebb5c6cf04b817d912be8d1952c050372c4305bd60c3ffbe595"}}