17 citations · 24 across the 16 of their papers we have counts for
17 papers
Test-Time Scaling for Video Diffusion Models via Diagnosis-Guided Candidate Recycling
Hangzhou He, Lunhao Duan, Shanshan Zhao +4
Recent video diffusion models have achieved remarkable generation quality, but high-fidelity results still largely depend on closed-source systems or costly large-scale infrastruct…
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…
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…
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…
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…
On the Duality Between Sharpness-Aware Minimization and Adversarial Training
Yihao Zhang, Hangzhou He, Jingyu Zhu +3
Adversarial Training (AT), which adversarially perturb the input samples during training, has been acknowledged as one of the most effective defenses against adversarial attacks, y…
Multi-level Asymmetric Contrastive Learning for Volumetric Medical Image Segmentation Pre-training
Shuang Zeng, Lei Zhu, Xinliang Zhang +10
Medical image segmentation is a fundamental yet challenging task due to the arduous process of acquiring large volumes of high-quality labeled data from experts. Contrastive learni…
Branches Mutual Promotion for End-to-End Weakly Supervised Semantic Segmentation
Lei Zhu, Hangzhou He, Xinliang Zhang +4
End-to-end weakly supervised semantic segmentation aims at optimizing a segmentation model in a single-stage training process based on only image annotations. Existing methods adop…