LongEmo: Towards Emotion Understanding and Reasoning in Long Videos

arXiv:2609.40079 · cs.CV, cs.AI · Submitted 2026-09-30 · Read on arXiv

cs.CV, cs.AI

Submitted: 2026-09-30

Updated: 2026-09-30

Terminology

• Benchmark — 26× in this paper · explained in 2 episodes
• Affective Computing — 8× in this paper · explained in 1 episode
• Agentic Framework — 4× in this paper · explained in 6 episodes
• Core — 4× in this paper · explained in 2 episodes
• Trace — 4× in this paper · explained in 2 episodes
• LLM judge — 3× in this paper · explained in 2 episodes
• Ablation — 2× in this paper · explained in 1 episode
• Alignment — 2× in this paper · explained in 1 episode
• Benchmarking — 2× in this paper · explained in 4 episodes
• Frustration — 2× in this paper · explained in 2 episodes
• Grounding — 2× in this paper · explained in 3 episodes
• Hallucination — 2× in this paper · explained in 4 episodes
• Multimodal Reasoning — 2× in this paper · explained in 5 episodes
• Redundancy — 2× in this paper · explained in 2 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Verification — 2× in this paper · explained in 2 episodes
• Ablation study — 1× in this paper · explained in 1 episode
• Accuracy — 1× in this paper · explained in 1 episode
• Agentic System — 1× in this paper · explained in 2 episodes
• Aggregation — 1× in this paper · explained in 1 episode
• Causal Reasoning — 1× in this paper · explained in 2 episodes
• Computational overhead — 1× in this paper · explained in 2 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• Graph Construction — 1× in this paper · explained in 3 episodes
• Human-in-the-Loop — 1× in this paper · explained in 3 episodes
• Instruction Tuning — 1× in this paper · explained in 2 episodes
• Multi-Agent Framework — 1× in this paper · explained in 3 episodes
• Regret — 1× in this paper · explained in 3 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Semi-supervised Learning — 1× in this paper · explained in 4 episodes
• Verifier — 1× in this paper · explained in 2 episodes
• alpha — 1× in this paper · explained in 1 episode

Sources

• OmniFocus: Query-Guided Modality-Balanced Token Compression for Omni-Modal Large Language Models
• AVOC: Enhancing Hour-Level Audio-Video Understanding in Omni-Modal LLMs via Retrieval-Inspired Token Compression
• Emotion-LLaMA: Multimodal Emotion Recognition and Reasoning with Instruction Tuning
• DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligence
• Video-MME: The First-Ever Comprehensive Evaluation Benchmark of Multi-modal LLMs in Video Analysis
• EmoTrans: A Benchmark for Understanding, Reasoning, and Predicting Emotion Transitions in Multimodal LLMs
• EmoBench-M: Benchmarking Emotional Intelligence for Multimodal Large Language Models
• Beyond Emotion Recognition: A Multi-Turn Multimodal Emotion Understanding and Reasoning Benchmark
• VideoChat-Flash: Hierarchical Compression for Long-Context Video Modeling
• MERBench: A Unified Evaluation Benchmark for Multimodal Emotion Recognition
• AffectGPT: A New Dataset, Model, and Benchmark for Emotion Understanding with Multimodal Large Language Models
• MER 2025: When Affective Computing Meets Large Language Models
• OV-MER: Towards Open-Vocabulary Multimodal Emotion Recognition
• Speak While Watching: Unleashing TRUE Real-Time Video Understanding Capability of Multimodal Large Language Models
• LongVideoAgent: Multi-Agent Reasoning with Long Videos
• Seeing, Listening, Remembering, and Reasoning: A Multimodal Agent with Long-Term Memory
• LongVU: Spatiotemporal Adaptive Compression for Long Video-Language Understanding
• LVBench: An Extreme Long Video Understanding Benchmark
• Think, Then Verify: A Hypothesis-Verification Multi-Agent Framework for Long Video Understanding
• Efficient Streaming Language Models with Attention Sinks

Related papers

• Loss Knows Best: Detecting Annotation Errors in Videos via Loss Trajectories
• AnchorWeave: World-Consistent Video Generation with Retrieved Local Spatial Memories
• Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift
• MambaX-Net: Dual-Input Mamba-Enhanced Cross-Attention Network for Longitudinal MRI Segmentation
• TeleOCR: Navigating Document Parsing Across Digital and Camera-Captured Documents
• A Survey on Efficient Vision-Language-Action Models