1 citations · 1 across the 7 of their papers we have counts for
10 papers
AdaTok: Adaptive Token Compression with Object-Aware Representations for Efficient Multimodal LLMs
Xinliang Zhang, Lei Zhu, Hangzhou He +5
Multimodal Large Language Models (MLLMs) have demonstrated substantial value in unified text-image understanding and reasoning, primarily by converting images into sequences of pat…
Chat-CBM: Towards Interactive Concept Bottleneck Models with Frozen Large Language Models
Hangzhou He, Lei Zhu, Kaiwen Li +5
Concept Bottleneck Models (CBMs) provide inherent interpretability by first predicting a set of human-understandable concepts and then mapping them to labels through a simple class…
Improve Retinal Artery/Vein Classification via Channel Couplin
Shuang Zeng, Chee Hong Lee, Kaiwen Li +6
Retinal vessel segmentation plays a vital role in analyzing fundus images for the diagnosis of systemic and ocular diseases. Building on this, classifying segmented vessels into ar…
Inter- and Intra-image Refinement for Few Shot Segmentation
Ourui Fu, Hangzhou He, Kaiwen Li +5
Deep neural networks for semantic segmentation rely on large-scale annotated datasets, leading to an annotation bottleneck that motivates few shot semantic segmentation (FSS) which…
Enhancing Image Restoration Transformer via Adaptive Translation Equivariance
JiaKui Hu, Zhengjian Yao, Lujia Jin +2
Translation equivariance is a fundamental inductive bias in image restoration, ensuring that translated inputs produce translated outputs. Attention mechanisms in modern restoratio…
Training-free Test-time Improvement for Explainable Medical Image Classification
Hangzhou He, Jiachen Tang, Lei Zhu +2
Deep learning-based medical image classification techniques are rapidly advancing in medical image analysis, making it crucial to develop accurate and trustworthy models that can b…