activity
20232026
most citedMasked Pre-training Enables Universal Zero-shot Denoiser

1 citations · 2 across the 5 of their papers we have counts for

collaborators

7 papers

cs.CV2026

MicroCLIP: Connecting Text and Microscopy Images via Contrastive Language--Image Pretraining

Yanran Ruan, Zhixiang Wei, Shiyao Hong +5

Understanding fluorescence microscopy images is fundamental to automating biological research. However, existing methods typically learn predefined concepts from closed-set data, l…

cs.CV2025

Rein++: Efficient Generalization and Adaptation for Semantic Segmentation with Vision Foundation Models

Zhixiang Wei, Xiaoxiao Ma, Ruishen Yan +5

Vision Foundation Models(VFMs) have achieved remarkable success in various computer vision tasks. However, their application to semantic segmentation is hindered by two significant…

cs.CV2025

HQ-CLIP: Leveraging Large Vision-Language Models to Create High-Quality Image-Text Datasets and CLIP Models

Zhixiang Wei, Guangting Wang, Xiaoxiao Ma +4

Large-scale but noisy image-text pair data have paved the way for the success of Contrastive Language-Image Pretraining (CLIP). As the foundation vision encoder, CLIP in turn serve…

cs.CV2024

STAR: Scale-wise Text-conditioned AutoRegressive image generation

Xiaoxiao Ma, Mohan Zhou, Tao Liang +5

We introduce STAR, a text-to-image model that employs a scale-wise auto-regressive paradigm. Unlike VAR, which is constrained to class-conditioned synthesis for images up to 256$\t…

cs.CV2024

Seed Optimization with Frozen Generator for Superior Zero-shot Low-light Enhancement

Yuxuan Gu, Yi Jin, Ben Wang +6

In this work, we observe that the generators, which are pre-trained on massive natural images, inherently hold the promising potential for superior low-light image enhancement agai…

cs.CV2024★ 1 cited

Masked Pre-training Enables Universal Zero-shot Denoiser

Xiaoxiao Ma, Zhixiang Wei, Yi Jin +5

In this work, we observe that model trained on vast general images via masking strategy, has been naturally embedded with their distribution knowledge, thus spontaneously attains t…

cs.CV2023★ 1 cited

Stronger, Fewer, & Superior: Harnessing Vision Foundation Models for Domain Generalized Semantic Segmentation

Zhixiang Wei, Lin Chen, Yi Jin +6

In this paper, we first assess and harness various Vision Foundation Models (VFMs) in the context of Domain Generalized Semantic Segmentation (DGSS). Driven by the motivation that…