1 citations · 1 across the 2 of their papers we have counts for
4 papers
Calibration and Transformation-Free Weight-Only LLMs Quantization via Dynamic Grouping
Xinzhe Zheng, Zhen-Qun Yang, Zishan Liu +4
Large Language Models (LLMs) deliver strong performance but are difficult to deploy under tight memory and compute constraints. Low-bit post-training quantization (PTQ) is a promis…
Efficiently Integrate Large Language Models with Visual Perception: A Survey from the Training Paradigm Perspective
Xiaorui Ma, Haoran Xie, S. Joe Qin
The integration of vision-language modalities has been a significant focus in multimodal learning, traditionally relying on Vision-Language Pretrained Models. However, with the adv…
CondAmbigQA: A Benchmark and Dataset for Conditional Ambiguous Question Answering
Zongxi Li, Yang Li, Haoran Xie +1
Users often assume that large language models (LLMs) share their cognitive alignment of context and intent, leading them to omit critical information in question-answering (QA) and…
MLLA-UNet: Mamba-like Linear Attention in an Efficient U-Shape Model for Medical Image Segmentation
Yufeng Jiang, Zongxi Li, Xiangyan Chen +2
Recent advancements in medical imaging have resulted in more complex and diverse images, with challenges such as high anatomical variability, blurred tissue boundaries, low organ c…