arXiv (All)
open access
Decoding Affective Nuances: Enhancing MLLMs via Hierarchical Emotion Reasoning and Contrastive Discriminative Pruning
While multimodal large language models (MLLMs) have demonstrated exceptional capabilities in objective understanding tasks, their performance in affective reasoning still falls significantly short of human standards. We attribute it to a central capability gap: MLLMs are difficult to reliably distin
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- OneEmo: A Unified Multimodal Reasoning Model for Emotion Perception, Understanding, and Interaction#974520
- LongEmo: Towards Emotion Understanding and Reasoning in Long Videos#1154348
- LongEmo: Towards Emotion Understanding and Reasoning in Long Videos#1136876
- Multi-Faceted Evaluation and Mitigation of Emotion Hallucinations in MLLMs#998706
- Do Large Language Models Have Emotions?#121596
Record · ID 1131983
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive