10 citations · 34 across the 8 of their papers we have counts for
8 papers · 1 filter
Omni-DETR: Omni-Supervised Object Detection with Transformers
Pei Wang, Zhaowei Cai, Hao Yang +4
We consider the problem of omni-supervised object detection, which can use unlabeled, fully labeled and weakly labeled annotations, such as image tags, counts, points, etc., for ob…
IMAGINE: Image Synthesis by Image-Guided Model Inversion
Pei Wang, Yijun Li, Krishna Kumar Singh +2
We introduce an inversion based method, denoted as IMAge-Guided model INvErsion (IMAGINE), to generate high-quality and diverse images from only a single training sample. We levera…
Rethinking and Improving the Robustness of Image Style Transfer
Pei Wang, Yijun Li, Nuno Vasconcelos
Extensive research in neural style transfer methods has shown that the correlation between features extracted by a pre-trained VGG network has a remarkable ability to capture the v…
Dynamic Transfer for Multi-Source Domain Adaptation
Yunsheng Li, Lu Yuan, Yinpeng Chen +2
Recent works of multi-source domain adaptation focus on learning a domain-agnostic model, of which the parameters are static. However, such a static model is difficult to handle co…
Non-uniform Motion Deblurring with Blurry Component Divided Guidance
Pei Wang, Wei Sun, Qingsen Yan +5
Blind image deblurring is a fundamental and challenging computer vision problem, which aims to recover both the blur kernel and the latent sharp image from only a blurry observatio…
Semantic-Guided Representation Enhancement for Self-supervised Monocular Trained Depth Estimation
Rui Li, Qing Mao, Pei Wang +4
Self-supervised depth estimation has shown its great effectiveness in producing high quality depth maps given only image sequences as input. However, its performance usually drops…