351 citations · 795 across the 27 of their papers we have counts for
9 papers · 1 filter
Composite Adversarial Attacks
Xiaofeng Mao, Yuefeng Chen, Shuhui Wang +3
Adversarial attack is a technique for deceiving Machine Learning (ML) models, which provides a way to evaluate the adversarial robustness. In practice, attack algorithms are artifi…
Heuristic Domain Adaptation
Shuhao Cui, Xuan Jin, Shuhui Wang +2
In visual domain adaptation (DA), separating the domain-specific characteristics from the domain-invariant representations is an ill-posed problem. Existing methods apply different…
Semantic Editing On Segmentation Map Via Multi-Expansion Loss
Jianfeng He, Xuchao Zhang, Shuo Lei +4
Semantic editing on segmentation map has been proposed as an intermediate interface for image generation, because it provides flexible and strong assistance in various image genera…
Label Decoupling Framework for Salient Object Detection
Jun Wei, Shuhui Wang, Zhe Wu +3
To get more accurate saliency maps, recent methods mainly focus on aggregating multi-level features from fully convolutional network (FCN) and introducing edge information as auxil…
Sharp Multiple Instance Learning for DeepFake Video Detection
Xiaodan Li, Yining Lang, Yuefeng Chen +5
With the rapid development of facial manipulation techniques, face forgery has received considerable attention in multimedia and computer vision community due to security concerns.…
Parsing-based View-aware Embedding Network for Vehicle Re-Identification
Dechao Meng, Liang Li, Xuejing Liu +6
Vehicle Re-Identification is to find images of the same vehicle from various views in the cross-camera scenario. The main challenges of this task are the large intra-instance dista…
State-Relabeling Adversarial Active Learning
Beichen Zhang, Liang Li, Shijie Yang +3
Active learning is to design label-efficient algorithms by sampling the most representative samples to be labeled by an oracle. In this paper, we propose a state relabeling adversa…
Gradually Vanishing Bridge for Adversarial Domain Adaptation
Shuhao Cui, Shuhui Wang, Junbao Zhuo +3
In unsupervised domain adaptation, rich domain-specific characteristics bring great challenge to learn domain-invariant representations. However, domain discrepancy is considered t…
Towards Discriminability and Diversity: Batch Nuclear-norm Maximization under Label Insufficient Situations
Shuhao Cui, Shuhui Wang, Junbao Zhuo +3
The learning of the deep networks largely relies on the data with human-annotated labels. In some label insufficient situations, the performance degrades on the decision boundary w…