activity
20202026
most citedGlobal Structure-Aware Diffusion Process for Low-Light Image Enhancement

43 citations · 90 across the 21 of their papers we have counts for

collaborators

25 papers

cs.CV2026

Diffusion Image Generation with Explicit Modeling of Data Manifold Geometry

Duoduo Xue, Zhiyu Zhu, Junhui Hou

Image generative models aim to sample data points from the underlying data manifold, a task that requires learning and decoding a dense, low-dimensional, and compact parameterizati…

cs.CV2026

Scaling Dense Event-Stream Pretraining from Visual Foundation Models

Zhiwen Chen, Junhui Hou, Zhiyu Zhu +2

Learning versatile, fine-grained representations from irregular event streams is pivotal yet nontrivial, primarily due to the heavy annotation that hinders scalability in dataset s…

cs.CV2025

Optimizing Multi-Modality Trackers via Significance-Regularized Tuning

Zhiwen Chen, Jinjian Wu, Zhiyu Zhu +3

This paper tackles the critical challenge of optimizing multi-modality trackers by effectively adapting pre-trained models for RGB data. Existing fine-tuning paradigms oscillate be…

cs.CV2025

Acc3D: Accelerating Single Image to 3D Diffusion Models via Edge Consistency Guided Score Distillation

Kendong Liu, Zhiyu Zhu, Hui Liu +1

We present Acc3D to tackle the challenge of accelerating the diffusion process to generate 3D models from single images. To derive high-quality reconstructions through few-step inf…

cs.CV2024

ResFlow: Fine-tuning Residual Optical Flow for Event-based High Temporal Resolution Motion Estimation

Qianang Zhou, Zhiyu Zhu, Junhui Hou +3

Event cameras hold significant promise for high-temporal-resolution (HTR) motion estimation. However, estimating event-based HTR optical flow faces two key challenges: the absence…

cs.CV2024

PrefPaint: Aligning Image Inpainting Diffusion Model with Human Preference

Kendong Liu, Zhiyu Zhu, Chuanhao Li +3

In this paper, we make the first attempt to align diffusion models for image inpainting with human aesthetic standards via a reinforcement learning framework, significantly improvi…