10 citations · 28 across the 6 of their papers we have counts for
6 papers
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…
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…
Solving Long-tailed Recognition with Deep Realistic Taxonomic Classifier
Tz-Ying Wu, Pedro Morgado, Pei Wang +2
Long-tail recognition tackles the natural non-uniformly distributed data in real-world scenarios. While modern classifiers perform well on populated classes, its performance degrad…
SCOUT: Self-aware Discriminant Counterfactual Explanations
Pei Wang, Nuno Vasconcelos
The problem of counterfactual visual explanations is considered. A new family of discriminant explanations is introduced. These produce heatmaps that attribute high scores to image…