6 citations · 23 across the 9 of their papers we have counts for
9 papers
Boosting Weakly-Supervised Temporal Action Localization with Text Information
Guozhang Li, De Cheng, Xinpeng Ding +3
Due to the lack of temporal annotation, current Weakly-supervised Temporal Action Localization (WTAL) methods are generally stuck into over-complete or incomplete localization. In…
Few-shot Face Image Translation via GAN Prior Distillation
Ruoyu Zhao, Mingrui Zhu, Xiaoyu Wang +1
Face image translation has made notable progress in recent years. However, when training on limited data, the performance of existing approaches significantly declines. Although so…
All-to-key Attention for Arbitrary Style Transfer
Mingrui Zhu, Xiao He, Nannan Wang +2
Attention-based arbitrary style transfer studies have shown promising performance in synthesizing vivid local style details. They typically use the all-to-all attention mechanism -…
Improving Adversarial Robustness via Mutual Information Estimation
Dawei Zhou, Nannan Wang, Xinbo Gao +4
Deep neural networks (DNNs) are found to be vulnerable to adversarial noise. They are typically misled by adversarial samples to make wrong predictions. To alleviate this negative…
Towards Semi-Supervised Deep Facial Expression Recognition with An Adaptive Confidence Margin
Hangyu Li, Nannan Wang, Xi Yang +2
Only parts of unlabeled data are selected to train models for most semi-supervised learning methods, whose confidence scores are usually higher than the pre-defined threshold (i.e.…
Semi-parametric Makeup Transfer via Semantic-aware Correspondence
Mingrui Zhu, Yun Yi, Nannan Wang +2
The large discrepancy between the source non-makeup image and the reference makeup image is one of the key challenges in makeup transfer. Conventional approaches for makeup transfe…
Improving White-box Robustness of Pre-processing Defenses via Joint Adversarial Training
Dawei Zhou, Nannan Wang, Xinbo Gao +4
Deep neural networks (DNNs) are vulnerable to adversarial noise. A range of adversarial defense techniques have been proposed to mitigate the interference of adversarial noise, amo…
Removing Adversarial Noise in Class Activation Feature Space
Dawei Zhou, Nannan Wang, Chunlei Peng +4
Deep neural networks (DNNs) are vulnerable to adversarial noise. Preprocessing based defenses could largely remove adversarial noise by processing inputs. However, they are typical…
Weakly Supervised Temporal Action Localization with Segment-Level Labels
Xinpeng Ding, Nannan Wang, Xinbo Gao +3
Temporal action localization presents a trade-off between test performance and annotation-time cost. Fully supervised methods achieve good performance with time-consuming boundary…