6 citations · 9 across the 10 of their papers we have counts for
10 papers
Context Enhancement with Reconstruction as Sequence for Unified Unsupervised Anomaly Detection
Hui-Yue Yang, Hui Chen, Lihao Liu +5
Unsupervised anomaly detection (AD) aims to train robust detection models using only normal samples, while can generalize well to unseen anomalies. Recent research focuses on a uni…
MaskMoE: Boosting Token-Level Learning via Routing Mask in Mixture-of-Experts
Zhenpeng Su, Zijia Lin, Xue Bai +8
Scaling the size of a model enhances its capabilities but significantly increases computation complexity. Mixture-of-Experts models (MoE) address the issue by allowing model size t…
Learn from the Learnt: Source-Free Active Domain Adaptation via Contrastive Sampling and Visual Persistence
Mengyao Lyu, Tianxiang Hao, Xinhao Xu +4
Domain Adaptation (DA) facilitates knowledge transfer from a source domain to a related target domain. This paper investigates a practical DA paradigm, namely Source data-Free Acti…
Debiased Novel Category Discovering and Localization
Juexiao Feng, Yuhong Yang, Yanchun Xie +6
In recent years, object detection in deep learning has experienced rapid development. However, most existing object detection models perform well only on closed-set datasets, ignor…
Confidence-based Visual Dispersal for Few-shot Unsupervised Domain Adaptation
Yizhe Xiong, Hui Chen, Zijia Lin +2
Unsupervised domain adaptation aims to transfer knowledge from a fully-labeled source domain to an unlabeled target domain. However, in real-world scenarios, providing abundant lab…
Consolidator: Mergeable Adapter with Grouped Connections for Visual Adaptation
Tianxiang Hao, Hui Chen, Yuchen Guo +1
Recently, transformers have shown strong ability as visual feature extractors, surpassing traditional convolution-based models in various scenarios. However, the success of vision…