7 papers
ViQ: Text-Aligned Visual Quantized Representations at Any Resolution
Xumin Yu, Zuyan Liu, Zhenyu Yang +5
A unified representation for text and vision is a natural pursuit, as it enables simpler multimodal modeling and more efficient training. However, representing images as discrete s…
SparseMM: Head Sparsity Emerges from Visual Concept Responses in MLLMs
Jiahui Wang, Zuyan Liu, Yongming Rao +1
Multimodal Large Language Models (MLLMs) are commonly derived by extending pre-trained Large Language Models (LLMs) with visual capabilities. In this work, we investigate how MLLMs…
Vision Generalist Model: A Survey
Ziyi Wang, Yongming Rao, Shuofeng Sun +8
Recently, we have witnessed the great success of the generalist model in natural language processing. The generalist model is a general framework trained with massive data and is a…
Oryx MLLM: On-Demand Spatial-Temporal Understanding at Arbitrary Resolution
Zuyan Liu, Yuhao Dong, Ziwei Liu +3
Visual data comes in various forms, ranging from small icons of just a few pixels to long videos spanning hours. Existing multi-modal LLMs usually standardize these diverse visual…
Coarse Correspondences Boost Spatial-Temporal Reasoning in Multimodal Language Model
Benlin Liu, Yuhao Dong, Yiqin Wang +6
Multimodal language models (MLLMs) are increasingly being applied in real-world environments, necessitating their ability to interpret 3D spaces and comprehend temporal dynamics. C…
Efficient Inference of Vision Instruction-Following Models with Elastic Cache
Zuyan Liu, Benlin Liu, Jiahui Wang +5
In the field of instruction-following large vision-language models (LVLMs), the efficient deployment of these models faces challenges, notably due to the high memory demands of the…