8 citations · 9 across the 10 of their papers we have counts for
10 papers
Compression and Retrieval: Implicit Memory Retrieval for Video World Models
Zhan Peng, Jie Ma, Huiqiang Sun +6
Video world models hold promise for simulating interactive environments, yet maintaining consistent long-term memory across complex camera trajectories remains a critical challenge…
Plug-and-Adapt: Multimodal Coreference Resolution at First Sight with a Pretrained Alignment Model
Jinghan Wu, Jing Li, Ivor W. Tsang +1
Visual information helps resolve ambiguity in coreference resolution, leading to notable performance gains. However, existing Multi-modal Coreference Resolution (MCR) methods requi…
Uncover and Unlearn Nuisances: Agnostic Fully Test-Time Adaptation
Ponhvoan Srey, Yaxin Shi, Hangwei Qian +2
Fully Test-Time Adaptation (FTTA) addresses domain shifts without access to source data and training protocols of the pre-trained models. Traditional strategies that align source a…
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…
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…
Sanitized Clustering against Confounding Bias
Yinghua Yao, Yuangang Pan, Jing Li +2
Real-world datasets inevitably contain biases that arise from different sources or conditions during data collection. Consequently, such inconsistency itself acts as a confounding…
Earning Extra Performance from Restrictive Feedbacks
Jing Li, Yuangang Pan, Yueming Lyu +3
Many machine learning applications encounter a situation where model providers are required to further refine the previously trained model so as to gratify the specific need of loc…
Taming Overconfident Prediction on Unlabeled Data from Hindsight
Jing Li, Yuangang Pan, Ivor W. Tsang
Minimizing prediction uncertainty on unlabeled data is a key factor to achieve good performance in semi-supervised learning (SSL). The prediction uncertainty is typically expressed…
Secure Metric Learning via Differential Pairwise Privacy
Jing Li, Yuangang Pan, Yulei Sui +1
Distance Metric Learning (DML) has drawn much attention over the last two decades. A number of previous works have shown that it performs well in measuring the similarities of indi…