8 citations · 17 across the 4 of their papers we have counts for
10 papers
Adversarial Attack and Defense in Deep Ranking
Mo Zhou, Le Wang, Zhenxing Niu +3
Deep Neural Network classifiers are vulnerable to adversarial attack, where an imperceptible perturbation could result in misclassification. However, the vulnerability of DNN-based…
ACSNet: Action-Context Separation Network for Weakly Supervised Temporal Action Localization
Ziyi Liu, Le Wang, Qilin Zhang +4
The object of Weakly-supervised Temporal Action Localization (WS-TAL) is to localize all action instances in an untrimmed video with only video-level supervision. Due to the lack o…
Practical Relative Order Attack in Deep Ranking
Mo Zhou, Le Wang, Zhenxing Niu +4
Recent studies unveil the vulnerabilities of deep ranking models, where an imperceptible perturbation can trigger dramatic changes in the ranking result. While previous attempts fo…
Two-Stream Consensus Network for Weakly-Supervised Temporal Action Localization
Yuanhao Zhai, Le Wang, Wei Tang +3
Weakly-supervised Temporal Action Localization (W-TAL) aims to classify and localize all action instances in an untrimmed video under only video-level supervision. However, without…
Adversarial Ranking Attack and Defense
Mo Zhou, Zhenxing Niu, Le Wang +2
Deep Neural Network (DNN) classifiers are vulnerable to adversarial attack, where an imperceptible perturbation could result in misclassification. However, the vulnerability of DNN…
Ladder Loss for Coherent Visual-Semantic Embedding
Mo Zhou, Zhenxing Niu, Le Wang +3
For visual-semantic embedding, the existing methods normally treat the relevance between queries and candidates in a bipolar way -- relevant or irrelevant, and all "irrelevant" can…