The field of multimodal video understanding is rapidly advancing, driven by innovations in large language models, reinforcement learning, and multi-agent systems. A key direction is the development of frameworks that integrate perception and reasoning, enabling more accurate and fine-grained video understanding. Another significant trend is the creation of benchmarks and datasets that assess the ability of models to reason about implicit world knowledge, physical causal reasoning, and fine-grained temporal perception. Notable papers in this area include SciEducator, which proposes a self-evolving multi-agent system for scientific video comprehension and education, and EgoVITA, which introduces a reinforcement learning framework for egocentric video reasoning. Other notable papers include Beyond Words and Pixels, which introduces a benchmark for implicit world knowledge reasoning, and VideoPerceiver, which enhances fine-grained temporal perception in video multimodal large language models. These advancements have the potential to significantly improve the accuracy and effectiveness of video understanding systems, with applications in areas such as education, advertising, and content creation.
Advances in Multimodal Video Understanding
Sources
SciEducator: Scientific Video Understanding and Educating via Deming-Cycle Multi-Agent System
EgoVITA: Learning to Plan and Verify for Egocentric Video Reasoning
Beyond Words and Pixels: A Benchmark for Implicit World Knowledge Reasoning in Generative Models
ViMix-14M: A Curated Multi-Source Video-Text Dataset with Long-Form, High-Quality Captions and Crawl-Free Access
ChineseVideoBench: Benchmarking Multi-modal Large Models for Chinese Video Question Answering
Alternating Perception-Reasoning for Hallucination-Resistant Video Understanding
VideoPerceiver: Enhancing Fine-Grained Temporal Perception in Video Multimodal Large Language Models
RAVEN++: Pinpointing Fine-Grained Violations in Advertisement Videos with Active Reinforcement Reasoning
VDC-Agent: When Video Detailed Captioners Evolve Themselves via Agentic Self-Reflection
VideoChat-M1: Collaborative Policy Planning for Video Understanding via Multi-Agent Reinforcement Learning
Vidi2: Large Multimodal Models for Video Understanding and Creation
LongVT: Incentivizing "Thinking with Long Videos" via Native Tool Calling
Towards an Effective Action-Region Tracking Framework for Fine-grained Video Action Recognition
Thinking With Bounding Boxes: Enhancing Spatio-Temporal Video Grounding via Reinforcement Fine-Tuning