7 papers
VideoThinker: Building Agentic VideoLLMs with LLM-Guided Tool Reasoning
Chenglin Li, Qianglong Chen, Feng Han +6
Long-form video understanding remains a fundamental challenge for current Video Large Language Models. Most existing models rely on static reasoning over uniformly sampled frames,…
VideoPro: Adaptive Program Reasoning for Long Video Understanding
Chenglin Li, Feng Han, Yikun Wang +9
Large language models (LLMs) have shown promise in generating program workflows for visual tasks. However, previous approaches often rely on closed-source models, lack systematic r…
VideoCogQA: A Controllable Benchmark for Evaluating Cognitive Abilities in Video-Language Models
Chenglin Li, Qianglong Chen, Zhi Li +2
Recent advancements in Large Video-Language Models (LVLMs) have led to promising results in multimodal video understanding. However, it remains unclear whether these models possess…
Iterative Zoom-In: Temporal Interval Exploration for Long Video Understanding
Chenglin Li, Qianglong Chen, fengtao +1
Multimodal Large Language Models (MLLMs) have shown strong performance in video understanding tasks. However, they continue to struggle with long-form videos because of an ineffici…
A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions
Lei Huang, Weijiang Yu, Weitao Ma +8
The emergence of large language models (LLMs) has marked a significant breakthrough in natural language processing (NLP), fueling a paradigm shift in information acquisition. Never…
Trends in Integration of Knowledge and Large Language Models: A Survey and Taxonomy of Methods, Benchmarks, and Applications
Zhangyin Feng, Weitao Ma, Weijiang Yu +7
Large language models (LLMs) exhibit superior performance on various natural language tasks, but they are susceptible to issues stemming from outdated data and domain-specific limi…