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Unified Multimodal Autoregressive Modeling with Shared Context-Visual Tokenizer is Key to Unification
Wujian Peng, Lingchen Meng, Yuxuan Cai +7
Unified Multimodal Modeling aims to integrate visual understanding and generation within a single system. However, existing approaches typically rely on two disparate visual tokeni…
Not All Tokens and Heads Are Equally Important: Dual-Level Attention Intervention for Hallucination Mitigation
Lexiang Tang, Xianwei Zhuang, Bang Yang +5
Large vision-language models (LVLMs) have demonstrated impressive capabilities across diverse multimodal tasks, yet they remain highly susceptible to visual hallucinations (VH), of…
VARGPT-v1.1: Improve Visual Autoregressive Large Unified Model via Iterative Instruction Tuning and Reinforcement Learning
Xianwei Zhuang, Yuxin Xie, Yufan Deng +5
In this work, we present VARGPT-v1.1, an advanced unified visual autoregressive model that builds upon our previous framework VARGPT. The model preserves the dual paradigm of next-…
VARGPT: Unified Understanding and Generation in a Visual Autoregressive Multimodal Large Language Model
Xianwei Zhuang, Yuxin Xie, Yufan Deng +4
We present VARGPT, a novel multimodal large language model (MLLM) that unifies visual understanding and generation within a single autoregressive framework. VARGPT employs a next-t…
VASparse: Towards Efficient Visual Hallucination Mitigation via Visual-Aware Token Sparsification
Xianwei Zhuang, Zhihong Zhu, Yuxin Xie +2
Large Vision-Language Models (LVLMs) may produce outputs that are unfaithful to reality, also known as visual hallucinations (VH), which significantly impedes their real-world usag…
Uncertainty-aware sign language video retrieval with probability distribution modeling
Xuan Wu, Hongxiang Li, Yuanjiang Luo +4
Sign language video retrieval plays a key role in facilitating information access for the deaf community. Despite significant advances in video-text retrieval, the complexity and i…