{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:B5IWA22HWF22Y7LAJILIQ2AVXI","short_pith_number":"pith:B5IWA22H","canonical_record":{"source":{"id":"2406.17815","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-06-25T05:54:07Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"80c4eaff7d39e2a80d28418b36a3cc0d2c00161428c3de0a3a3577c9b034c84f","abstract_canon_sha256":"1ca50ea0ae2c0949221de534362d348b99788a957d3cf5249a0dabe90c1b898e"},"schema_version":"1.0"},"canonical_sha256":"0f51606b47b175ac7d604a16886815ba3dd8f8a827a916423c2798de56c26376","source":{"kind":"arxiv","id":"2406.17815","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2406.17815","created_at":"2026-07-05T09:04:37Z"},{"alias_kind":"arxiv_version","alias_value":"2406.17815v2","created_at":"2026-07-05T09:04:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.17815","created_at":"2026-07-05T09:04:37Z"},{"alias_kind":"pith_short_12","alias_value":"B5IWA22HWF22","created_at":"2026-07-05T09:04:37Z"},{"alias_kind":"pith_short_16","alias_value":"B5IWA22HWF22Y7LA","created_at":"2026-07-05T09:04:37Z"},{"alias_kind":"pith_short_8","alias_value":"B5IWA22H","created_at":"2026-07-05T09:04:37Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:B5IWA22HWF22Y7LAJILIQ2AVXI","target":"record","payload":{"canonical_record":{"source":{"id":"2406.17815","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-06-25T05:54:07Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"80c4eaff7d39e2a80d28418b36a3cc0d2c00161428c3de0a3a3577c9b034c84f","abstract_canon_sha256":"1ca50ea0ae2c0949221de534362d348b99788a957d3cf5249a0dabe90c1b898e"},"schema_version":"1.0"},"canonical_sha256":"0f51606b47b175ac7d604a16886815ba3dd8f8a827a916423c2798de56c26376","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:04:37.263434Z","signature_b64":"JKSkZj/XR3NKXpRHWLjdGuQpzMq5v2EgAk2mljVVyLTc15gpsIVLquX0+nyykfi+sffKPxXny4rkCO+X3FrcBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0f51606b47b175ac7d604a16886815ba3dd8f8a827a916423c2798de56c26376","last_reissued_at":"2026-07-05T09:04:37.262980Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:04:37.262980Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2406.17815","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":"2026-07-05T09:04:37Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"FpGt2SyG2toKf37PzNotYR3Kj/vZ468ngIkzY/Ga01wnl8bLvgnntOD/WAEhoQgA7NNCaE+WkPraLh1Z9XGLAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T19:06:32.519509Z"},"content_sha256":"42ef6772fd2f1b8a213418ad3264b062cea2be7e70c62482965a79312412d0f9","schema_version":"1.0","event_id":"sha256:42ef6772fd2f1b8a213418ad3264b062cea2be7e70c62482965a79312412d0f9"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:B5IWA22HWF22Y7LAJILIQ2AVXI","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"SUM: Saliency Unification through Mamba for Visual Attention Modeling","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Alireza Hosseini, Amirhossein Kazerouni, Babak Taati, Michael Brudno, Saeed Akhavan","submitted_at":"2024-06-25T05:54:07Z","abstract_excerpt":"Visual attention modeling, important for interpreting and prioritizing visual stimuli, plays a significant role in applications such as marketing, multimedia, and robotics. Traditional saliency prediction models, especially those based on Convolutional Neural Networks (CNNs) or Transformers, achieve notable success by leveraging large-scale annotated datasets. However, the current state-of-the-art (SOTA) models that use Transformers are computationally expensive. Additionally, separate models are often required for each image type, lacking a unified approach. In this paper, we propose Saliency"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.17815","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2406.17815/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2026-07-05T09:04:37Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"aeWf1eZ4LwM7Mq4ZMjTkl6dFgCpfNy6/KLRApK8eDBpJPpBD5fyLS3yq6r+HIWR2M0ipydELMojfsgiZ02FkCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T19:06:32.520009Z"},"content_sha256":"bdd53cc13454a3b11f0a45d063f8a01d2ef4408d2a616e3ec8be800e59177e20","schema_version":"1.0","event_id":"sha256:bdd53cc13454a3b11f0a45d063f8a01d2ef4408d2a616e3ec8be800e59177e20"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/B5IWA22HWF22Y7LAJILIQ2AVXI/bundle.json","state_url":"https://pith.science/pith/B5IWA22HWF22Y7LAJILIQ2AVXI/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/B5IWA22HWF22Y7LAJILIQ2AVXI/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":"2026-08-06T19:06:32Z","links":{"resolver":"https://pith.science/pith/B5IWA22HWF22Y7LAJILIQ2AVXI","bundle":"https://pith.science/pith/B5IWA22HWF22Y7LAJILIQ2AVXI/bundle.json","state":"https://pith.science/pith/B5IWA22HWF22Y7LAJILIQ2AVXI/state.json","well_known_bundle":"https://pith.science/.well-known/pith/B5IWA22HWF22Y7LAJILIQ2AVXI/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:B5IWA22HWF22Y7LAJILIQ2AVXI","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":"1ca50ea0ae2c0949221de534362d348b99788a957d3cf5249a0dabe90c1b898e","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-06-25T05:54:07Z","title_canon_sha256":"80c4eaff7d39e2a80d28418b36a3cc0d2c00161428c3de0a3a3577c9b034c84f"},"schema_version":"1.0","source":{"id":"2406.17815","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2406.17815","created_at":"2026-07-05T09:04:37Z"},{"alias_kind":"arxiv_version","alias_value":"2406.17815v2","created_at":"2026-07-05T09:04:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.17815","created_at":"2026-07-05T09:04:37Z"},{"alias_kind":"pith_short_12","alias_value":"B5IWA22HWF22","created_at":"2026-07-05T09:04:37Z"},{"alias_kind":"pith_short_16","alias_value":"B5IWA22HWF22Y7LA","created_at":"2026-07-05T09:04:37Z"},{"alias_kind":"pith_short_8","alias_value":"B5IWA22H","created_at":"2026-07-05T09:04:37Z"}],"graph_snapshots":[{"event_id":"sha256:bdd53cc13454a3b11f0a45d063f8a01d2ef4408d2a616e3ec8be800e59177e20","target":"graph","created_at":"2026-07-05T09:04:37Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2406.17815/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Visual attention modeling, important for interpreting and prioritizing visual stimuli, plays a significant role in applications such as marketing, multimedia, and robotics. Traditional saliency prediction models, especially those based on Convolutional Neural Networks (CNNs) or Transformers, achieve notable success by leveraging large-scale annotated datasets. However, the current state-of-the-art (SOTA) models that use Transformers are computationally expensive. Additionally, separate models are often required for each image type, lacking a unified approach. In this paper, we propose Saliency","authors_text":"Alireza Hosseini, Amirhossein Kazerouni, Babak Taati, Michael Brudno, Saeed Akhavan","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-06-25T05:54:07Z","title":"SUM: Saliency Unification through Mamba for Visual Attention Modeling"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.17815","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":"sha256:42ef6772fd2f1b8a213418ad3264b062cea2be7e70c62482965a79312412d0f9","target":"record","created_at":"2026-07-05T09:04:37Z","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":"1ca50ea0ae2c0949221de534362d348b99788a957d3cf5249a0dabe90c1b898e","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-06-25T05:54:07Z","title_canon_sha256":"80c4eaff7d39e2a80d28418b36a3cc0d2c00161428c3de0a3a3577c9b034c84f"},"schema_version":"1.0","source":{"id":"2406.17815","kind":"arxiv","version":2}},"canonical_sha256":"0f51606b47b175ac7d604a16886815ba3dd8f8a827a916423c2798de56c26376","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0f51606b47b175ac7d604a16886815ba3dd8f8a827a916423c2798de56c26376","first_computed_at":"2026-07-05T09:04:37.262980Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:04:37.262980Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"JKSkZj/XR3NKXpRHWLjdGuQpzMq5v2EgAk2mljVVyLTc15gpsIVLquX0+nyykfi+sffKPxXny4rkCO+X3FrcBw==","signature_status":"signed_v1","signed_at":"2026-07-05T09:04:37.263434Z","signed_message":"canonical_sha256_bytes"},"source_id":"2406.17815","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:42ef6772fd2f1b8a213418ad3264b062cea2be7e70c62482965a79312412d0f9","sha256:bdd53cc13454a3b11f0a45d063f8a01d2ef4408d2a616e3ec8be800e59177e20"],"state_sha256":"a99082a0aec9ffd0b6bc3ec225ab9fe9f844c11de71787b3b8b4d49d6b1b1cf7"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"rGgRTQFizJNU7v+xt+pZxnxAVknrhVHbEqAhiuBSSGmWlcTbvd3O/r52DcfIq03YpXW8w9WlxecahGeWIemuCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-06T19:06:32.524206Z","bundle_sha256":"47b774d04e87e9edcf56707b6371f4bb5bf4776dc3d835f71a56f5179cf75f37"}}