1 citations · 1 across the 5 of their papers we have counts for
8 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…
Novel Extraction of Discriminative Fine-Grained Feature to Improve Retinal Vessel Segmentation
Shuang Zeng, Chee Hong Lee, Micky C Nnamdi +9
Retinal vessel segmentation is a vital early detection method for several severe ocular diseases. Despite significant progress in retinal vessel segmentation with the advancement o…
SuperCL: Superpixel Guided Contrastive Learning for Medical Image Segmentation Pre-training
Shuang Zeng, Lei Zhu, Xinliang Zhang +2
Medical image segmentation is a critical yet challenging task, primarily due to the difficulty of obtaining extensive datasets of high-quality, expert-annotated images. Contrastive…