2 citations · 2 across the 10 of their papers we have counts for
12 papers
FREAK: A Fine-grained Hallucination Evaluation Benchmark for Advanced MLLMs
Zhihan Yin, Jianxin Liang, Yueqian Wang +3
Multimodal Large Language Models (MLLMs) suffer from hallucinations. Existing hallucination evaluation benchmarks are often limited by over-simplified tasks leading to saturated me…
MMDuet2: Enhancing Proactive Interaction of Video MLLMs with Multi-Turn Reinforcement Learning
Yueqian Wang, Songxiang Liu, Disong Wang +4
Recent advances in video multimodal large language models (Video MLLMs) have significantly enhanced video understanding and multi-modal interaction capabilities. While most existin…
K-frames: Scene-Driven Any-k Keyframe Selection for long video understanding
Yifeng Yao, Yike Yun, Jing Wang +6
Multimodal Large Language Models (MLLMs) have demonstrated significant capabilities in image understanding, but long-video are constrained by context windows and computational cost…
Do LLMs "Feel"? Emotion Circuits Discovery and Control
Chenxi Wang, Yixuan Zhang, Ruiji Yu +8
As the demand for emotional intelligence in large language models (LLMs) grows, a key challenge lies in understanding the internal mechanisms that give rise to emotional expression…
Beyond Isolated Facts: Synthesizing Narrative and Grounded Supervision for VideoQA
Jianxin Liang, Tan Yue, Yuxuan Wang +4
The performance of Video Question Answering (VideoQA) models is fundamentally constrained by the nature of their supervision, which typically consists of isolated, factual question…
ProactiveVideoQA: A Comprehensive Benchmark Evaluating Proactive Interactions in Video Large Language Models
Yueqian Wang, Xiaojun Meng, Yifan Wang +2
With the growing research focus on multimodal dialogue systems, the capability for proactive interaction is gradually gaining recognition. As an alternative to conventional turn-by…