8 citations · 8 across the 10 of their papers we have counts for
10 papers
Reliable Neural Collapse Approximation for Open-World Test-Time Adaptation
Jia-Qi Lin, Yuangang Pan, Chang-Dong Wang +3
Test-Time Adaptation (TTA) methods aim to bridge the domain gap between the source and target domains. However, traditional TTA methods become ineffective when the label distributi…
SelectTSL: Prompt-Guided Selective Target Sound Localization in Complex Scenarios
Ziyang Jiang, Yu Chen, Zexu Pan +5
Humans can selectively attend to a target sound and estimate its direction in complex scenarios, whereas such selective localization remains challenging for current deep learning-b…
Leveraging Scene Context with Dual Networks for Sequential User Behavior Modeling
Xu Chen, Yunmeng Shu, Yuangang Pan +6
Modeling sequential user behaviors for future behavior prediction is crucial in improving user's information retrieval experience. Recent studies highlight the importance of incorp…
Towards Harmless Rawlsian Fairness Regardless of Demographic Prior
Xuanqian Wang, Jing Li, Ivor W. Tsang +1
Due to privacy and security concerns, recent advancements in group fairness advocate for model training regardless of demographic information. However, most methods still require p…
Alpha and Prejudice: Improving -sized Worst-case Fairness via Intrinsic Reweighting
Jing Li, Yinghua Yao, Yuangang Pan +3
Worst-case fairness with off-the-shelf demographics achieves group parity by maximizing the model utility of the worst-off group. Nevertheless, demographic information is often una…
Learning Multi-Branch Cooperation for Enhanced Click-Through Rate Prediction at Taobao
Xu Chen, Zida Cheng, Yuangang Pan +6
Existing click-through rate (CTR) prediction works have studied the role of feature interaction through a variety of techniques. Each interaction technique exhibits its own strengt…
Self-Assessed Generation: Trustworthy Label Generation for Optical Flow and Stereo Matching in Real-world
Han Ling, Yinghui Sun, Quansen Sun +2
A significant challenge facing current optical flow and stereo methods is the difficulty in generalizing them well to the real world. This is mainly due to the high costs required…
PROUD: PaRetO-gUided Diffusion Model for Multi-objective Generation
Yinghua Yao, Yuangang Pan, Jing Li +2
Recent advancements in the realm of deep generative models focus on generating samples that satisfy multiple desired properties. However, prevalent approaches optimize these proper…
HCL: Hybrid and Cooperative Contrastive Learning for Cross-lingual Spoken Language Understanding
Bowen Xing, Ivor W. Tsang
State-of-the-art model for zero-shot cross-lingual spoken language understanding performs cross-lingual unsupervised contrastive learning to achieve the label-agnostic semantic ali…
Exploiting Contextual Target Attributes for Target Sentiment Classification
Bowen Xing, Ivor W. Tsang
Existing PTLM-based models for TSC can be categorized into two groups: 1) fine-tuning-based models that adopt PTLM as the context encoder; 2) prompting-based models that transfer t…