RACER: Reflective Agent Coupling Query Interpretation and Tool-Based Retrieval for Frame Selection in Long Video Understanding
cs.CV, cs.AI
Submitted: 2026-10-06
Updated: 2026-10-06
Sources
- Qwen3-VL Technical Report
- Qwen2.5-VL Technical Report
- Video Anomaly Detection and Explanation via Large Language Models
- GIFT: Global Irreplaceability Frame Targeting for Efficient Video Understanding
- CREST: Curvature-Regulated Event-Centric Sampling for Efficient Long-Video Understanding
- InternVideo3: Agentify Foundation Models with Multimodal Contextual Reasoning
- Towards Fast and Effective Long Video Understanding of Multimodal Large Language Models via Adaptive Quasi-Gaussian Sampling
- LLaVA-Video: Video Instruction Tuning With Synthetic Data
- InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models
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