16 citations · 35 across the 13 of their papers we have counts for
16 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…
CapField-OPD: Learning Continuous Capability Fields via Joint-Anchored Multi-Teacher On-Policy Distillation for Flow Models
Pengyang Ling, Yujie Zhou, Jiazi Bu +6
Reward-specialized post-training produces strong experts for flow-based generative models, while multi-teacher on-policy distillation (OPD) consolidates their capabilities into a s…
Pave-GRPO: Beyond Instantaneous Guidance through Principled Average Velocity Decomposition
Pengyang Ling, Jiazi Bu, Yujie Zhou +6
Group Relative Policy Optimization(GRPO) has emerged as an effective paradigm for aligning flow-based generative models with human preferences. However, the high cost of group roll…
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…
Single Image Dehazing Using Scene Depth Ordering
Pengyang Ling, Huaian Chen, Xiao Tan +2
Images captured in hazy weather generally suffer from quality degradation, and many dehazing methods have been developed to solve this problem. However, single image dehazing probl…
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…
MotionClone: Training-Free Motion Cloning for Controllable Video Generation
Pengyang Ling, Jiazi Bu, Pan Zhang +6
Motion-based controllable video generation offers the potential for creating captivating visual content. Existing methods typically necessitate model training to encode particular…
Real-Time 4K Super-Resolution of Compressed AVIF Images. AIS 2024 Challenge Survey
Marcos V. Conde, Zhijun Lei, Wen Li +72
This paper introduces a novel benchmark as part of the AIS 2024 Real-Time Image Super-Resolution (RTSR) Challenge, which aims to upscale compressed images from 540p to 4K resolutio…
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…
FreeDrag: Feature Dragging for Reliable Point-based Image Editing
Pengyang Ling, Lin Chen, Pan Zhang +3
To serve the intricate and varied demands of image editing, precise and flexible manipulation in image content is indispensable. Recently, Drag-based editing methods have gained im…
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…