activity
20242026
most citedUniversal Image Restoration Pre-training via Degradation Classification

1 citations · 1 across the 10 of their papers we have counts for

collaborators

11 papers

cs.CV2026

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…

cs.CV2026

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…

cs.CV2026

Geometry-as-context: Modulating Explicit 3D in Scene-consistent Video Generation to Geometry Context

JiaKui Hu, Jialun Liu, Liying Yang +7

Scene-consistent video generation aims to create videos that explore 3D scenes based on a camera trajectory. Previous methods rely on video generation models with external memory f…

cs.CV2026

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…

cs.CV2025

Omni-View: Unlocking How Generation Facilitates Understanding in Unified 3D Model based on Multiview images

JiaKui Hu, Shanshan Zhao, Qing-Guo Chen +6

This paper presents Omni-View, which extends the unified multimodal understanding and generation to 3D scenes based on multiview images, exploring the principle that "generation fa…

cs.CV2025

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…

cs.CV2025

Auto-Regressively Generating Multi-View Consistent Images

JiaKui Hu, Yuxiao Yang, Jialun Liu +3

Generating multi-view images from human instructions is crucial for 3D content creation. The primary challenges involve maintaining consistency across multiple views and effectivel…

cs.CV2025

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…

cs.CV2025

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…

cs.CV2025★ 1 cited

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…

cs.CV2024

Spike2Former: Efficient Spiking Transformer for High-performance Image Segmentation

Zhenxin Lei, Man Yao, Jiakui Hu +4

Spiking Neural Networks (SNNs) have a low-power advantage but perform poorly in image segmentation tasks. The reason is that directly converting neural networks with complex archit…