1 citations · 2 across the 11 of their papers we have counts for
11 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…
Evaluation of Winning Solutions of 2025 Low Power Computer Vision Challenge
Zihao Ye, Yung-Hsiang Lu, Xiao Hu +14
The IEEE Low-Power Computer Vision Challenge (LPCVC) aims to promote the development of efficient vision models for edge devices, balancing accuracy with constraints such as latenc…
Narrative Weaver: Towards Controllable Long-Range Visual Consistency with Multi-Modal Conditioning
Zhengjian Yao, Yongzhi Li, Xinyuan Gao +3
We present "Narrative Weaver", a novel framework that addresses a fundamental challenge in generative AI: achieving multi-modal controllable, long-range, and consistent visual cont…
Bridging Degradation Discrimination and Generation for Universal Image Restoration
JiaKui Hu, Zhengjian Yao, Lujia Jin +1
Universal image restoration is a critical task in low-level vision, requiring the model to remove various degradations from low-quality images to produce clean images with rich det…
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…
Universal Image Restoration Pre-training via Masked Degradation Classification
JiaKui Hu, Zhengjian Yao, Lujia Jin +2
This study introduces a Masked Degradation Classification Pre-Training method (MaskDCPT), designed to facilitate the classification of degradation types in input images, leading to…
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…
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…
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…
Universal Image Restoration Pre-training via Degradation Classification
JiaKui Hu, Lujia Jin, Zhengjian Yao +1
This paper proposes the Degradation Classification Pre-Training (DCPT), which enables models to learn how to classify the degradation type of input images for universal image resto…