arXiv CS
open access
LongEmo: Towards Emotion Understanding and Reasoning in Long Videos
While recent Multimodal Large Language Models (MLLMs) have shown promise in affective computing, their reasoning capabilities are largely confined to short video clips with limited interactions. However, real-world emotions are not merely isolated instantaneous reactions but dynamic and cumulative p
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
- LongEmo: Towards Emotion Understanding and Reasoning in Long Videos#1154348
- Decoding Affective Nuances: Enhancing MLLMs via Hierarchical Emotion Reasoning and Contrastive Discriminative Pruning#1131983
- VideoTIR: Accurate Understanding for Long Videos with Efficient Tool-Integrated Reasoning#987622
- EM^2Mem: Event-Centric Multimodal Memory for Large Language Models#819990
- COMET: Contrastive Motion-Enhanced Temporal Reasoning for Video Multimodal Large Language Models#662209
Record · ID 1136876
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