1 code implementation • 7 Jun 2021 • Mo Zhou, Le Wang, Zhenxing Niu, Qilin Zhang, Nanning Zheng, Gang Hua
In this paper, we propose two attacks against deep ranking systems, i. e., Candidate Attack and Query Attack, that can raise or lower the rank of chosen candidates by adversarial perturbations.
no code implementations • 28 Mar 2021 • Ziyi Liu, Le Wang, Qilin Zhang, Wei Tang, Junsong Yuan, Nanning Zheng, Gang Hua
In this paper, we introduce an Action-Context Separation Network (ACSNet) that explicitly takes into account context for accurate action localization.
Ranked #7 on Weakly Supervised Action Localization on THUMOS’14
Video Polyp Segmentation Weakly Supervised Action Localization +2
2 code implementations • ICCV 2021 • Mo Zhou, Le Wang, Zhenxing Niu, Qilin Zhang, Yinghui Xu, Nanning Zheng, Gang Hua
In this paper, we formulate a new adversarial attack against deep ranking systems, i. e., the Order Attack, which covertly alters the relative order among a selected set of candidates according to an attacker-specified permutation, with limited interference to other unrelated candidates.
no code implementations • 1 Jan 2021 • Mo Zhou, Le Wang, Zhenxing Niu, Qilin Zhang, Xu Yinghui, Nanning Zheng, Gang Hua
The objective of this paper is to formalize and practically implement a new adversarial attack against deep ranking systems, i. e., the Order Attack, which covertly alters the relative order of a selected set of candidates according to a permutation vector predefined by the attacker, with only limited interference to other unrelated candidates.
no code implementations • ECCV 2020 • Yuanhao Zhai, Le Wang, Wei Tang, Qilin Zhang, Junsong Yuan, Gang Hua
Weakly-supervised Temporal Action Localization (W-TAL) aims to classify and localize all action instances in an untrimmed video under only video-level supervision.
Ranked #12 on Weakly Supervised Action Localization on THUMOS14
Vocal Bursts Valence Prediction Weakly Supervised Action Localization +2
3 code implementations • ECCV 2020 • Mo Zhou, Zhenxing Niu, Le Wang, Qilin Zhang, Gang Hua
In this paper, we propose two attacks against deep ranking systems, i. e., Candidate Attack and Query Attack, that can raise or lower the rank of chosen candidates by adversarial perturbations.
2 code implementations • 18 Nov 2019 • Mo Zhou, Zhenxing Niu, Le Wang, Zhanning Gao, Qilin Zhang, Gang Hua
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" candidates are uniformly pushed away from the query by an equal margin in the embedding space, regardless of their various proximity to the query.
no code implementations • 16 May 2019 • Chi Zhang, Yuehu Liu, Ying Wu, Qilin Zhang, Le Wang
In the pipeline, the estimated shape is refined by the shape prior from the given depth map under the estimated pose.
no code implementations • 8 May 2018 • Yunfeng Wang, Wengang Zhou, Qilin Zhang, Houqiang Li
Visual attributes in individual video frames, such as the presence of characteristic objects and scenes, offer substantial information for action recognition in videos.
no code implementations • 8 May 2018 • Yunfeng Wang, Wengang Zhou, Qilin Zhang, Xiaotian Zhu, Houqiang Li
Termed "Weighted Multi-Region Convolutional Neural Network" (WMR ConvNet), the proposed system is LSTM-free, and is based on 2D ConvNet that does not require the accumulation of video frames for 3D ConvNet filtering.
no code implementations • 19 Mar 2018 • Jinliang Zang, Le Wang, Ziyi Liu, Qilin Zhang, Zhenxing Niu, Gang Hua, Nanning Zheng
Research in human action recognition has accelerated significantly since the introduction of powerful machine learning tools such as Convolutional Neural Networks (CNNs).
no code implementations • 30 Jan 2018 • Jie Huang, Wengang Zhou, Qilin Zhang, Houqiang Li, Weiping Li
Worse still, isolated SLR methods typically require strenuous labeling of each word separately in a sentence, severely limiting the amount of attainable training data.