{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:PZICDWFLTSB7WDLP5ZJ4K74OV3","short_pith_number":"pith:PZICDWFL","canonical_record":{"source":{"id":"1811.10763","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-11-27T01:10:34Z","cross_cats_sorted":[],"title_canon_sha256":"8b7ee74a2523c22e887c6437da172017028ec33f2a40d2d63cc02a0a7421b69c","abstract_canon_sha256":"508abc5100f7852d085dbfc290ec3885cbf75fb9b10a618ed71ef7b76306851e"},"schema_version":"1.0"},"canonical_sha256":"7e5021d8ab9c83fb0d6fee53c57f8eaee1656f0ce5e110bb1f7419701403c925","source":{"kind":"arxiv","id":"1811.10763","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1811.10763","created_at":"2026-05-17T23:59:47Z"},{"alias_kind":"arxiv_version","alias_value":"1811.10763v1","created_at":"2026-05-17T23:59:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1811.10763","created_at":"2026-05-17T23:59:47Z"},{"alias_kind":"pith_short_12","alias_value":"PZICDWFLTSB7","created_at":"2026-05-18T12:32:46Z"},{"alias_kind":"pith_short_16","alias_value":"PZICDWFLTSB7WDLP","created_at":"2026-05-18T12:32:46Z"},{"alias_kind":"pith_short_8","alias_value":"PZICDWFL","created_at":"2026-05-18T12:32:46Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:PZICDWFLTSB7WDLP5ZJ4K74OV3","target":"record","payload":{"canonical_record":{"source":{"id":"1811.10763","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-11-27T01:10:34Z","cross_cats_sorted":[],"title_canon_sha256":"8b7ee74a2523c22e887c6437da172017028ec33f2a40d2d63cc02a0a7421b69c","abstract_canon_sha256":"508abc5100f7852d085dbfc290ec3885cbf75fb9b10a618ed71ef7b76306851e"},"schema_version":"1.0"},"canonical_sha256":"7e5021d8ab9c83fb0d6fee53c57f8eaee1656f0ce5e110bb1f7419701403c925","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:59:47.602692Z","signature_b64":"gnaw2bX0raIPjuoGB+qTpY8Ku2i0X8BbCE/7fqOyFaZhgBDP9be8etpPWWmXsMD+5CcsQh6TBegtMYg8MryZAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7e5021d8ab9c83fb0d6fee53c57f8eaee1656f0ce5e110bb1f7419701403c925","last_reissued_at":"2026-05-17T23:59:47.602267Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:59:47.602267Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1811.10763","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":"2026-05-17T23:59:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"gM2duJHW8YWaERYXZONFmfd2lUYJVzTIH4HsVERoFIvPxp1cbJINuK73mjV2si7lo9g6rXJDMHWF3MCijdjdCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T16:27:02.717723Z"},"content_sha256":"75fd488c32684ef39619bfa1a461d868eb35cccccb62bf25479a97148dea965d","schema_version":"1.0","event_id":"sha256:75fd488c32684ef39619bfa1a461d868eb35cccccb62bf25479a97148dea965d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:PZICDWFLTSB7WDLP5ZJ4K74OV3","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Quality-Aware Multimodal Saliency Detection via Deep Reinforcement Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bin Luo, Chenglong Li, Jin Tang, Rui Yang, Tao Sun, Xiao Wang","submitted_at":"2018-11-27T01:10:34Z","abstract_excerpt":"Incorporating various modes of information into the machine learning procedure is becoming a new trend. And data from various source can provide more information than single one no matter they are heterogeneous or homogeneous. Existing deep learning based algorithms usually directly concatenate features from each domain to represent the input data. Seldom of them take the quality of data into consideration which is a key issue in related multimodal problems. In this paper, we propose an efficient quality-aware deep neural network to model the weight of data from each domain using deep reinforc"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1811.10763","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":"2026-05-17T23:59:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"mPHyJGx3mTnoow4OmcoK8B86Ef0z3o1Q9abg9j9+I87t/ZdPalvMX2hacF0ivZE/eO3Lm41SEpk8pAnu0JozDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T16:27:02.718407Z"},"content_sha256":"eb96d38dade525c3f75197134fb3edf88e80719c7d0d4880e4470f6abdef4c61","schema_version":"1.0","event_id":"sha256:eb96d38dade525c3f75197134fb3edf88e80719c7d0d4880e4470f6abdef4c61"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/PZICDWFLTSB7WDLP5ZJ4K74OV3/bundle.json","state_url":"https://pith.science/pith/PZICDWFLTSB7WDLP5ZJ4K74OV3/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/PZICDWFLTSB7WDLP5ZJ4K74OV3/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":"2026-05-25T16:27:02Z","links":{"resolver":"https://pith.science/pith/PZICDWFLTSB7WDLP5ZJ4K74OV3","bundle":"https://pith.science/pith/PZICDWFLTSB7WDLP5ZJ4K74OV3/bundle.json","state":"https://pith.science/pith/PZICDWFLTSB7WDLP5ZJ4K74OV3/state.json","well_known_bundle":"https://pith.science/.well-known/pith/PZICDWFLTSB7WDLP5ZJ4K74OV3/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:PZICDWFLTSB7WDLP5ZJ4K74OV3","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":"508abc5100f7852d085dbfc290ec3885cbf75fb9b10a618ed71ef7b76306851e","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-11-27T01:10:34Z","title_canon_sha256":"8b7ee74a2523c22e887c6437da172017028ec33f2a40d2d63cc02a0a7421b69c"},"schema_version":"1.0","source":{"id":"1811.10763","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1811.10763","created_at":"2026-05-17T23:59:47Z"},{"alias_kind":"arxiv_version","alias_value":"1811.10763v1","created_at":"2026-05-17T23:59:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1811.10763","created_at":"2026-05-17T23:59:47Z"},{"alias_kind":"pith_short_12","alias_value":"PZICDWFLTSB7","created_at":"2026-05-18T12:32:46Z"},{"alias_kind":"pith_short_16","alias_value":"PZICDWFLTSB7WDLP","created_at":"2026-05-18T12:32:46Z"},{"alias_kind":"pith_short_8","alias_value":"PZICDWFL","created_at":"2026-05-18T12:32:46Z"}],"graph_snapshots":[{"event_id":"sha256:eb96d38dade525c3f75197134fb3edf88e80719c7d0d4880e4470f6abdef4c61","target":"graph","created_at":"2026-05-17T23:59:47Z","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":"Incorporating various modes of information into the machine learning procedure is becoming a new trend. And data from various source can provide more information than single one no matter they are heterogeneous or homogeneous. Existing deep learning based algorithms usually directly concatenate features from each domain to represent the input data. Seldom of them take the quality of data into consideration which is a key issue in related multimodal problems. In this paper, we propose an efficient quality-aware deep neural network to model the weight of data from each domain using deep reinforc","authors_text":"Bin Luo, Chenglong Li, Jin Tang, Rui Yang, Tao Sun, Xiao Wang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-11-27T01:10:34Z","title":"Quality-Aware Multimodal Saliency Detection via Deep Reinforcement Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1811.10763","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":"sha256:75fd488c32684ef39619bfa1a461d868eb35cccccb62bf25479a97148dea965d","target":"record","created_at":"2026-05-17T23:59:47Z","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":"508abc5100f7852d085dbfc290ec3885cbf75fb9b10a618ed71ef7b76306851e","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-11-27T01:10:34Z","title_canon_sha256":"8b7ee74a2523c22e887c6437da172017028ec33f2a40d2d63cc02a0a7421b69c"},"schema_version":"1.0","source":{"id":"1811.10763","kind":"arxiv","version":1}},"canonical_sha256":"7e5021d8ab9c83fb0d6fee53c57f8eaee1656f0ce5e110bb1f7419701403c925","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"7e5021d8ab9c83fb0d6fee53c57f8eaee1656f0ce5e110bb1f7419701403c925","first_computed_at":"2026-05-17T23:59:47.602267Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:59:47.602267Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"gnaw2bX0raIPjuoGB+qTpY8Ku2i0X8BbCE/7fqOyFaZhgBDP9be8etpPWWmXsMD+5CcsQh6TBegtMYg8MryZAA==","signature_status":"signed_v1","signed_at":"2026-05-17T23:59:47.602692Z","signed_message":"canonical_sha256_bytes"},"source_id":"1811.10763","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:75fd488c32684ef39619bfa1a461d868eb35cccccb62bf25479a97148dea965d","sha256:eb96d38dade525c3f75197134fb3edf88e80719c7d0d4880e4470f6abdef4c61"],"state_sha256":"8c6aa91375fd61c56b8652be29d25b2acb3cad1efc38c55e180a890d2051e866"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"IJOpWK4C+jqDDDqT5o+adTXFsjDOmypea+fqCG078ptx8206XBl9lfvqLjr7yi/sFL6DqqykCREess3nc+NJCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-25T16:27:02.722420Z","bundle_sha256":"e2a8897655f49fd4515bc362491f30230e5c177b9798bdfb56108edc1e576941"}}