209 citations · 408 across the 24 of their papers we have counts for
16 papers · 1 filter
Domain Enhanced Arbitrary Image Style Transfer via Contrastive Learning
Yuxin Zhang, Fan Tang, Weiming Dong +4
In this work, we tackle the challenging problem of arbitrary image style transfer using a novel style feature representation learning method. A suitable style representation, as a…
CrossRectify: Leveraging Disagreement for Semi-supervised Object Detection
Chengcheng Ma, Xingjia Pan, Qixiang Ye +3
Semi-supervised object detection has recently achieved substantial progress. As a mainstream solution, the self-labeling-based methods train the detector on both labeled data and u…
Reciprocal Normalization for Domain Adaptation
Zhiyong Huang, Kekai Sheng, Ke Li +5
Batch normalization (BN) is widely used in modern deep neural networks, which has been shown to represent the domain-related knowledge, and thus is ineffective for cross-domain tas…
Evo-ViT: Slow-Fast Token Evolution for Dynamic Vision Transformer
Yifan Xu, Zhijie Zhang, Mengdan Zhang +6
Vision transformers (ViTs) have recently received explosive popularity, but the huge computational cost is still a severe issue. Since the computation complexity of ViT is quadrati…
User-Guided Personalized Image Aesthetic Assessment based on Deep Reinforcement Learning
Pei Lv, Jianqi Fan, Xixi Nie +5
Personalized image aesthetic assessment (PIAA) has recently become a hot topic due to its usefulness in a wide variety of applications such as photography, film and television, e-c…
StyTr: Image Style Transfer with Transformers
Yingying Deng, Fan Tang, Weiming Dong +4
The goal of image style transfer is to render an image with artistic features guided by a style reference while maintaining the original content. Owing to the locality in convoluti…
Towards Corruption-Agnostic Robust Domain Adaptation
Yifan Xu, Kekai Sheng, Weiming Dong +3
Big progress has been achieved in domain adaptation in decades. Existing works are always based on an ideal assumption that testing target domain are i.i.d. with training target do…
On Evolving Attention Towards Domain Adaptation
Kekai Sheng, Ke Li, Xiawu Zheng +5
Towards better unsupervised domain adaptation (UDA). Recently, researchers propose various domain-conditioned attention modules and make promising progresses. However, considering…
Effective Label Propagation for Discriminative Semi-Supervised Domain Adaptation
Zhiyong Huang, Kekai Sheng, Weiming Dong +5
Semi-supervised domain adaptation (SSDA) methods have demonstrated great potential in large-scale image classification tasks when massive labeled data are available in the source d…
Arbitrary Video Style Transfer via Multi-Channel Correlation
Yingying Deng, Fan Tang, Weiming Dong +3
Video style transfer is getting more attention in AI community for its numerous applications such as augmented reality and animation productions. Compared with traditional image st…
Dynamic Refinement Network for Oriented and Densely Packed Object Detection
Xingjia Pan, Yuqiang Ren, Kekai Sheng +5
Object detection has achieved remarkable progress in the past decade. However, the detection of oriented and densely packed objects remains challenging because of following inheren…
Distribution Aligned Multimodal and Multi-Domain Image Stylization
Minxuan Lin, Fan Tang, Weiming Dong +3
Multimodal and multi-domain stylization are two important problems in the field of image style transfer. Currently, there are few methods that can perform both multimodal and multi…
Arbitrary Style Transfer via Multi-Adaptation Network
Yingying Deng, Fan Tang, Weiming Dong +3
Arbitrary style transfer is a significant topic with research value and application prospect. A desired style transfer, given a content image and referenced style painting, would r…
Multi-Attribute Guided Painting Generation
Minxuan Lin, Yingying Deng, Fan Tang +2
Controllable painting generation plays a pivotal role in image stylization. Currently, the control way of style transfer is subject to exemplar-based reference or a random one-hot…
Revisiting Image Aesthetic Assessment via Self-Supervised Feature Learning
Kekai Sheng, Weiming Dong, Menglei Chai +6
Visual aesthetic assessment has been an active research field for decades. Although latest methods have achieved promising performance on benchmark datasets, they typically rely on…
Image Retargetability
Fan Tang, Weiming Dong, Yiping Meng +4
Real-world applications could benefit from the ability to automatically retarget an image to different aspect ratios and resolutions, while preserving its visually and semantically…