{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:GYMU6O3PTPFIMQDSHZYA7PYQZZ","short_pith_number":"pith:GYMU6O3P","schema_version":"1.0","canonical_sha256":"36194f3b6f9bca8640723e700fbf10ce7ff8f636fb853b29540403466a3aff33","source":{"kind":"arxiv","id":"2507.14024","version":1},"attestation_state":"computed","paper":{"title":"Moodifier: MLLM-Enhanced Emotion-Driven Image Editing","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jiarong Ye, Sharon X. Huang","submitted_at":"2025-07-18T15:52:39Z","abstract_excerpt":"Bridging emotions and visual content for emotion-driven image editing holds great potential in creative industries, yet precise manipulation remains challenging due to the abstract nature of emotions and their varied manifestations across different contexts. We tackle this challenge with an integrated approach consisting of three complementary components. First, we introduce MoodArchive, an 8M+ image dataset with detailed hierarchical emotional annotations generated by LLaVA and partially validated by human evaluators. Second, we develop MoodifyCLIP, a vision-language model fine-tuned on MoodA"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2507.14024","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2025-07-18T15:52:39Z","cross_cats_sorted":[],"title_canon_sha256":"57c0d2788b8d420e03aa3cf4b34064c6938013ff9c585d0b4408d9193198bbc2","abstract_canon_sha256":"f0ed68bf4808e0ea3cc1a5fee12869a786c1dc2a3e20a657fddefe3ebb2f8e17"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:39:27.121234Z","signature_b64":"4zuZOwugKe6HyQaTyA+9hNeAjw5i2Go+6e4KyBnU5NvCNDySsG+cYy3fNirXrIc4wD5hcJDmeTlQoo3veOjhBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"36194f3b6f9bca8640723e700fbf10ce7ff8f636fb853b29540403466a3aff33","last_reissued_at":"2026-07-05T11:39:27.120737Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:39:27.120737Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Moodifier: MLLM-Enhanced Emotion-Driven Image Editing","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jiarong Ye, Sharon X. Huang","submitted_at":"2025-07-18T15:52:39Z","abstract_excerpt":"Bridging emotions and visual content for emotion-driven image editing holds great potential in creative industries, yet precise manipulation remains challenging due to the abstract nature of emotions and their varied manifestations across different contexts. We tackle this challenge with an integrated approach consisting of three complementary components. First, we introduce MoodArchive, an 8M+ image dataset with detailed hierarchical emotional annotations generated by LLaVA and partially validated by human evaluators. Second, we develop MoodifyCLIP, a vision-language model fine-tuned on MoodA"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.14024","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2507.14024/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2507.14024","created_at":"2026-07-05T11:39:27.120806+00:00"},{"alias_kind":"arxiv_version","alias_value":"2507.14024v1","created_at":"2026-07-05T11:39:27.120806+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.14024","created_at":"2026-07-05T11:39:27.120806+00:00"},{"alias_kind":"pith_short_12","alias_value":"GYMU6O3PTPFI","created_at":"2026-07-05T11:39:27.120806+00:00"},{"alias_kind":"pith_short_16","alias_value":"GYMU6O3PTPFIMQDS","created_at":"2026-07-05T11:39:27.120806+00:00"},{"alias_kind":"pith_short_8","alias_value":"GYMU6O3P","created_at":"2026-07-05T11:39:27.120806+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/GYMU6O3PTPFIMQDSHZYA7PYQZZ","json":"https://pith.science/pith/GYMU6O3PTPFIMQDSHZYA7PYQZZ.json","graph_json":"https://pith.science/api/pith-number/GYMU6O3PTPFIMQDSHZYA7PYQZZ/graph.json","events_json":"https://pith.science/api/pith-number/GYMU6O3PTPFIMQDSHZYA7PYQZZ/events.json","paper":"https://pith.science/paper/GYMU6O3P"},"agent_actions":{"view_html":"https://pith.science/pith/GYMU6O3PTPFIMQDSHZYA7PYQZZ","download_json":"https://pith.science/pith/GYMU6O3PTPFIMQDSHZYA7PYQZZ.json","view_paper":"https://pith.science/paper/GYMU6O3P","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2507.14024&json=true","fetch_graph":"https://pith.science/api/pith-number/GYMU6O3PTPFIMQDSHZYA7PYQZZ/graph.json","fetch_events":"https://pith.science/api/pith-number/GYMU6O3PTPFIMQDSHZYA7PYQZZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GYMU6O3PTPFIMQDSHZYA7PYQZZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GYMU6O3PTPFIMQDSHZYA7PYQZZ/action/storage_attestation","attest_author":"https://pith.science/pith/GYMU6O3PTPFIMQDSHZYA7PYQZZ/action/author_attestation","sign_citation":"https://pith.science/pith/GYMU6O3PTPFIMQDSHZYA7PYQZZ/action/citation_signature","submit_replication":"https://pith.science/pith/GYMU6O3PTPFIMQDSHZYA7PYQZZ/action/replication_record"}},"created_at":"2026-07-05T11:39:27.120806+00:00","updated_at":"2026-07-05T11:39:27.120806+00:00"}