16 citations · 33 across the 9 of their papers we have counts for
11 papers
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…
Towards Better & Faster Autoregressive Image Generation: From the Perspective of Entropy
Xiaoxiao Ma, Feng Zhao, Pengyang Ling +6
In this work, we first revisit the sampling issues in current autoregressive (AR) image generation models and identify that image tokens, unlike text tokens, exhibit lower informat…
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…
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…
CrossEarth: Geospatial Vision Foundation Model for Domain Generalizable Remote Sensing Semantic Segmentation
Ziyang Gong, Zhixiang Wei, Di Wang +13
The field of Remote Sensing Domain Generalization (RSDG) has emerged as a critical and valuable research frontier, focusing on developing models that generalize effectively across…
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…
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…
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…
Disentangle then Parse:Night-time Semantic Segmentation with Illumination Disentanglement
Zhixiang Wei, Lin Chen, Tao Tu +3
Most prior semantic segmentation methods have been developed for day-time scenes, while typically underperforming in night-time scenes due to insufficient and complicated lighting…
Deliberated Domain Bridging for Domain Adaptive Semantic Segmentation
Lin Chen, Zhixiang Wei, Xin Jin +4
In unsupervised domain adaptation (UDA), directly adapting from the source to the target domain usually suffers significant discrepancies and leads to insufficient alignment. Thus,…
Reusing the Task-specific Classifier as a Discriminator: Discriminator-free Adversarial Domain Adaptation
Lin Chen, Huaian Chen, Zhixiang Wei +4
Adversarial learning has achieved remarkable performances for unsupervised domain adaptation (UDA). Existing adversarial UDA methods typically adopt an additional discriminator to…