17 citations · 24 across the 15 of their papers we have counts for
15 papers
RADA: Region-Aware Dual-encoder Auxiliary learning for Barely-supervised Medical Image Segmentation
Shuang Zeng, Boxu Xie, Lei Zhu +6
Deep learning has greatly advanced medical image segmentation, but its success relies heavily on fully supervised learning, which requires dense annotations that are costly and tim…
Bridging Information Asymmetry: A Hierarchical Framework for Deterministic Blind Face Restoration
Zhengjian Yao, Jiakui Hu, Kaiwen Li +5
Blind face restoration remains a persistent challenge due to the inherent ill-posedness of reconstructing holistic structures from severely constrained observations. Current genera…
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…
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…
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…
Exploiting Inherent Class Label: Towards Robust Scribble Supervised Semantic Segmentation
Xinliang Zhang, Lei Zhu, Shuang Zeng +5
Scribble-based weakly supervised semantic segmentation leverages only a few annotated pixels as labels to train a segmentation model, presenting significant potential for reducing…
V2C-CBM: Building Concept Bottlenecks with Vision-to-Concept Tokenizer
Hangzhou He, Lei Zhu, Xinliang Zhang +3
Concept Bottleneck Models (CBMs) offer inherent interpretability by initially translating images into human-comprehensible concepts, followed by a linear combination of these conce…
Low-Rank Mixture-of-Experts for Continual Medical Image Segmentation
Qian Chen, Lei Zhu, Hangzhou He +4
The primary goal of continual learning (CL) task in medical image segmentation field is to solve the "catastrophic forgetting" problem, where the model totally forgets previously l…
Scaling the Codebook Size of VQGAN to 100,000 with a Utilization Rate of 99%
Lei Zhu, Fangyun Wei, Yanye Lu +1
In the realm of image quantization exemplified by VQGAN, the process encodes images into discrete tokens drawn from a codebook with a predefined size. Recent advancements, particul…
Beyond Text: Frozen Large Language Models in Visual Signal Comprehension
Lei Zhu, Fangyun Wei, Yanye Lu
In this work, we investigate the potential of a large language model (LLM) to directly comprehend visual signals without the necessity of fine-tuning on multi-modal datasets. The f…
Scribble Hides Class: Promoting Scribble-Based Weakly-Supervised Semantic Segmentation with Its Class Label
Xinliang Zhang, Lei Zhu, Hangzhou He +2
Scribble-based weakly-supervised semantic segmentation using sparse scribble supervision is gaining traction as it reduces annotation costs when compared to fully annotated alterna…