284 citations · 637 across the 43 of their papers we have counts for
7 papers · 1 filter
Topology-aware Convolutional Neural Network for Efficient Skeleton-based Action Recognition
Kailin Xu, Fanfan Ye, Qiaoyong Zhong +1
In the context of skeleton-based action recognition, graph convolutional networks (GCNs) have been rapidly developed, whereas convolutional neural networks (CNNs) have received les…
Hindsight Reward Tweaking via Conditional Deep Reinforcement Learning
Ning Wei, Jiahua Liang, Di Xie +1
Designing optimal reward functions has been desired but extremely difficult in reinforcement learning (RL). When it comes to modern complex tasks, sophisticated reward functions ar…
TransForensics: Image Forgery Localization with Dense Self-Attention
Jing Hao, Zhixin Zhang, Shicai Yang +2
Nowadays advanced image editing tools and technical skills produce tampered images more realistically, which can easily evade image forensic systems and make authenticity verificat…
Divide-and-Assemble: Learning Block-wise Memory for Unsupervised Anomaly Detection
Jinlei Hou, Yingying Zhang, Qiaoyong Zhong +3
Reconstruction-based methods play an important role in unsupervised anomaly detection in images. Ideally, we expect a perfect reconstruction for normal samples and poor reconstruct…
Modulating Localization and Classification for Harmonized Object Detection
Taiheng Zhang, Qiaoyong Zhong, Shiliang Pu +1
Object detection involves two sub-tasks, i.e. localizing objects in an image and classifying them into various categories. For existing CNN-based detectors, we notice the widesprea…
Self-Supervised Noisy Label Learning for Source-Free Unsupervised Domain Adaptation
Weijie Chen, Luojun Lin, Shicai Yang +4
It is a strong prerequisite to access source data freely in many existing unsupervised domain adaptation approaches. However, source data is agnostic in many practical scenarios du…
Box Re-Ranking: Unsupervised False Positive Suppression for Domain Adaptive Pedestrian Detection
Weijie Chen, Yilu Guo, Shicai Yang +7
False positive is one of the most serious problems brought by agnostic domain shift in domain adaptive pedestrian detection. However, it is impossible to label each box in countles…