5 papers
UGround: Towards Unified Visual Grounding with Unrolled Transformers
Rui Qian, Xin Yin, Chuanhang Deng +4
We present UGround, a \textbf{U}nified visual \textbf{Ground}ing paradigm that dynamically selects intermediate layers across \textbf{U}nrolled transformers as ``mask as prompt,''…
Reasoning to Attend: Try to Understand How <SEG> Token Works
Rui Qian, Xin Yin, Dejing Dou
Current Large Multimodal Models (LMMs) empowered visual grounding typically rely on tokens as a text prompt to jointly optimize the vision-language model (e.g., LL…
BootSeer: Analyzing and Mitigating Initialization Bottlenecks in Large-Scale LLM Training
Rui Li, Xiaoyun Zhi, Jinxin Chi +14
Large Language Models (LLMs) have become a cornerstone of modern AI, driving breakthroughs in natural language processing and expanding into multimodal jobs involving images, audio…
Pre-training CLIP against Data Poisoning with Optimal Transport-based Matching and Alignment
Tong Zhang, Kuofeng Gao, Jiawang Bai +5
Recent studies have shown that Contrastive Language-Image Pre-training (CLIP) models are threatened by targeted data poisoning and backdoor attacks due to massive training image-ca…
SepPrune: Structured Pruning for Efficient Deep Speech Separation
Yuqi Li, Kai Li, Xin Yin +6
Although deep learning has substantially advanced speech separation in recent years, most existing studies continue to prioritize separation quality while overlooking computational…