9.2k citations · 16.2k across the 175 of their papers we have counts for
24 papers · 1 filter
MetaF2N: Blind Image Super-Resolution by Learning Efficient Model Adaptation from Faces
Zhicun Yin, Ming Liu, Xiaoming Li +3
Due to their highly structured characteristics, faces are easier to recover than natural scenes for blind image super-resolution. Therefore, we can extract the degradation represen…
Aggregating Nearest Sharp Features via Hybrid Transformers for Video Deblurring
Wei Shang, Dongwei Ren, Yi Yang +1
Video deblurring methods, aiming at recovering consecutive sharp frames from a given blurry video, usually assume that the input video suffers from consecutively blurry frames. How…
Cross-Consistent Deep Unfolding Network for Adaptive All-In-One Video Restoration
Yuanshuo Cheng, Mingwen Shao, Yecong Wan +3
Existing Video Restoration (VR) methods always necessitate the individual deployment of models for each adverse weather to remove diverse adverse weather degradations, lacking the…
Ref-Diff: Zero-shot Referring Image Segmentation with Generative Models
Minheng Ni, Yabo Zhang, Kailai Feng +3
Zero-shot referring image segmentation is a challenging task because it aims to find an instance segmentation mask based on the given referring descriptions, without training on th…
VQ-Font: Few-Shot Font Generation with Structure-Aware Enhancement and Quantization
Mingshuai Yao, Yabo Zhang, Xianhui Lin +2
Few-shot font generation is challenging, as it needs to capture the fine-grained stroke styles from a limited set of reference glyphs, and then transfer to other characters, which…
Towards Instance-adaptive Inference for Federated Learning
Chun-Mei Feng, Kai Yu, Nian Liu +3
Federated learning (FL) is a distributed learning paradigm that enables multiple clients to learn a powerful global model by aggregating local training. However, the performance of…
Diverse Data Augmentation with Diffusions for Effective Test-time Prompt Tuning
Chun-Mei Feng, Kai Yu, Yong Liu +2
Benefiting from prompt tuning, recent years have witnessed the promising performance of pre-trained vision-language models, e.g., CLIP, on versatile downstream tasks. In this paper…
UniMAE: Multi-modal Masked Autoencoders with Unified 3D Representation for 3D Perception in Autonomous Driving
Jian Zou, Tianyu Huang, Guanglei Yang +4
Masked Autoencoders (MAE) play a pivotal role in learning potent representations, delivering outstanding results across various 3D perception tasks essential for autonomous driving…
Latent Code Augmentation Based on Stable Diffusion for Data-free Substitute Attacks
Mingwen Shao, Lingzhuang Meng, Yuanjian Qiao +2
Since the training data of the target model is not available in the black-box substitute attack, most recent schemes utilize GANs to generate data for training the substitute model…
Improving Transferability of Adversarial Examples via Bayesian Attacks
Qizhang Li, Yiwen Guo, Xiaochen Yang +2
The transferability of adversarial examples allows for the attack on unknown deep neural networks (DNNs), posing a serious threat to many applications and attracting great attentio…
DRM-IR: Task-Adaptive Deep Unfolding Network for All-In-One Image Restoration
Yuanshuo Cheng, Mingwen Shao, Yecong Wan +1
Existing All-In-One image restoration (IR) methods usually lack flexible modeling on various types of degradation, thus impeding the restoration performance. To achieve All-In-One…
RBSR: Efficient and Flexible Recurrent Network for Burst Super-Resolution
Renlong Wu, Zhilu Zhang, Shuohao Zhang +2
Burst super-resolution (BurstSR) aims at reconstructing a high-resolution (HR) image from a sequence of low-resolution (LR) and noisy images, which is conducive to enhancing the im…
Inferring and Leveraging Parts from Object Shape for Improving Semantic Image Synthesis
Yuxiang Wei, Zhilong Ji, Xiaohe Wu +3
Despite the progress in semantic image synthesis, it remains a challenging problem to generate photo-realistic parts from input semantic map. Integrating part segmentation map can…
ControlVideo: Training-free Controllable Text-to-Video Generation
Yabo Zhang, Yuxiang Wei, Dongsheng Jiang +3
Text-driven diffusion models have unlocked unprecedented abilities in image generation, whereas their video counterpart still lags behind due to the excessive training cost of temp…
Self-supervised Learning to Bring Dual Reversed Rolling Shutter Images Alive
Wei Shang, Dongwei Ren, Chaoyu Feng +3
Modern consumer cameras usually employ the rolling shutter (RS) mechanism, where images are captured by scanning scenes row-by-row, yielding RS distortions for dynamic scenes. To c…
Associating Spatially-Consistent Grouping with Text-supervised Semantic Segmentation
Yabo Zhang, Zihao Wang, Jun Hao Liew +4
In this work, we investigate performing semantic segmentation solely through the training on image-sentence pairs. Due to the lack of dense annotations, existing text-supervised me…
Improving Adversarial Transferability via Intermediate-level Perturbation Decay
Qizhang Li, Yiwen Guo, Wangmeng Zuo +1
Intermediate-level attacks that attempt to perturb feature representations following an adversarial direction drastically have shown favorable performance in crafting transferable…
Learning Federated Visual Prompt in Null Space for MRI Reconstruction
Chun-Mei Feng, Bangjun Li, Xinxing Xu +3
Federated Magnetic Resonance Imaging (MRI) reconstruction enables multiple hospitals to collaborate distributedly without aggregating local data, thereby protecting patient privacy…
Joint Video Multi-Frame Interpolation and Deblurring under Unknown Exposure Time
Wei Shang, Dongwei Ren, Yi Yang +3
Natural videos captured by consumer cameras often suffer from low framerate and motion blur due to the combination of dynamic scene complexity, lens and sensor imperfection, and le…
Spatially Adaptive Self-Supervised Learning for Real-World Image Denoising
Junyi Li, Zhilu Zhang, Xiaoyu Liu +4
Significant progress has been made in self-supervised image denoising (SSID) in the recent few years. However, most methods focus on dealing with spatially independent noise, and t…
Learning Generative Structure Prior for Blind Text Image Super-resolution
Xiaoming Li, Wangmeng Zuo, Chen Change Loy
Blind text image super-resolution (SR) is challenging as one needs to cope with diverse font styles and unknown degradation. To address the problem, existing methods perform charac…
Towards Universal Vision-language Omni-supervised Segmentation
Bowen Dong, Jiaxi Gu, Jianhua Han +2
Existing open-world universal segmentation approaches usually leverage CLIP and pre-computed proposal masks to treat open-world segmentation tasks as proposal classification. Howev…
ELITE: Encoding Visual Concepts into Textual Embeddings for Customized Text-to-Image Generation
Yuxiang Wei, Yabo Zhang, Zhilong Ji +3
In addition to the unprecedented ability in imaginary creation, large text-to-image models are expected to take customized concepts in image generation. Existing works generally le…
Making Substitute Models More Bayesian Can Enhance Transferability of Adversarial Examples
Qizhang Li, Yiwen Guo, Wangmeng Zuo +1
The transferability of adversarial examples across deep neural networks (DNNs) is the crux of many black-box attacks. Many prior efforts have been devoted to improving the transfer…