1 citations · 2 across the 10 of their papers we have counts for
10 papers
What If Consensus Lies? Selective-Complementary Reinforcement Learning at Test Time
Dong Yan, Jian Liang, Yanbo Wang +3
Test-Time Reinforcement Learning (TTRL) enables Large Language Models (LLMs) to enhance reasoning capabilities on unlabeled test streams by deriving pseudo-rewards from majority vo…
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…
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…
ReasVQA: Advancing VideoQA with Imperfect Reasoning Process
Jianxin Liang, Xiaojun Meng, Huishuai Zhang +3
Video Question Answering (VideoQA) is a challenging task that requires understanding complex visual and temporal relationships within videos to answer questions accurately. In this…
Friends-MMC: A Dataset for Multi-modal Multi-party Conversation Understanding
Yueqian Wang, Xiaojun Meng, Yuxuan Wang +3
Multi-modal multi-party conversation (MMC) is a less studied yet important topic of research due to that it well fits real-world scenarios and thus potentially has more widely-used…
VideoLLM Knows When to Speak: Enhancing Time-Sensitive Video Comprehension with Video-Text Duet Interaction Format
Yueqian Wang, Xiaojun Meng, Yuxuan Wang +4
Recent researches on video large language models (VideoLLM) predominantly focus on model architectures and training datasets, leaving the interaction format between the user and th…
Understanding Multimodal Hallucination with Parameter-Free Representation Alignment
Yueqian Wang, Jianxin Liang, Yuxuan Wang +2
Hallucination is a common issue in Multimodal Large Language Models (MLLMs), yet the underlying principles remain poorly understood. In this paper, we investigate which components…
End-to-End Video Question Answering with Frame Scoring Mechanisms and Adaptive Sampling
Jianxin Liang, Xiaojun Meng, Yueqian Wang +3
Video Question Answering (VideoQA) has emerged as a challenging frontier in the field of multimedia processing, requiring intricate interactions between visual and textual modaliti…
HawkEye: Training Video-Text LLMs for Grounding Text in Videos
Yueqian Wang, Xiaojun Meng, Jianxin Liang +3
Video-text Large Language Models (video-text LLMs) have shown remarkable performance in answering questions and holding conversations on simple videos. However, they perform almost…
Efficient Temporal Extrapolation of Multimodal Large Language Models with Temporal Grounding Bridge
Yuxuan Wang, Yueqian Wang, Pengfei Wu +4
Despite progress in multimodal large language models (MLLMs), the challenge of interpreting long-form videos in response to linguistic queries persists, largely due to the ineffici…