most citedALUM: Adversarial Data Uncertainty Modeling from Latent Model Uncertainty Compensation

1 citations · 2 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2024

DASK: Distribution Rehearsing via Adaptive Style Kernel Learning for Exemplar-Free Lifelong Person Re-Identification

Kunlun Xu, Chenghao Jiang, Peixi Xiong +2

Lifelong person re-identification (LReID) is an important but challenging task that suffers from catastrophic forgetting due to significant domain gaps between training steps. Exis…

cs.CV2024

CAPrompt: Cyclic Prompt Aggregation for Pre-Trained Model Based Class Incremental Learning

Qiwei Li, Jiahuan Zhou

Recently, prompt tuning methods for pre-trained models have demonstrated promising performance in Class Incremental Learning (CIL). These methods typically involve learning task-sp…

cs.CV2023

A Grammatical Compositional Model for Video Action Detection

Zhijun Zhang, Xu Zou, Jiahuan Zhou +2

Analysis of human actions in videos demands understanding complex human dynamics, as well as the interaction between actors and context. However, these interaction relationships us…

cs.LG20231 cited

ALUM: Adversarial Data Uncertainty Modeling from Latent Model Uncertainty Compensation

Wei Wei, Jiahuan Zhou, Hongze Li +1

It is critical that the models pay attention not only to accuracy but also to the certainty of prediction. Uncertain predictions of deep models caused by noisy data raise significa…

cs.LG20231 cited

Beyond Empirical Risk Minimization: Local Structure Preserving Regularization for Improving Adversarial Robustness

Wei Wei, Jiahuan Zhou, Ying Wu

It is broadly known that deep neural networks are susceptible to being fooled by adversarial examples with perturbations imperceptible by humans. Various defenses have been propose…