31 citations · 49 across the 9 of their papers we have counts for
9 papers
Delving into Identify-Emphasize Paradigm for Combating Unknown Bias
Bowen Zhao, Chen Chen, Qian-Wei Wang +2
Dataset biases are notoriously detrimental to model robustness and generalization. The identify-emphasize paradigm appears to be effective in dealing with unknown biases. However,…
DELTA: degradation-free fully test-time adaptation
Bowen Zhao, Chen Chen, Shu-Tao Xia
Fully test-time adaptation aims at adapting a pre-trained model to the test stream during real-time inference, which is urgently required when the test distribution differs from th…
Controller-Guided Partial Label Consistency Regularization with Unlabeled Data
Qian-Wei Wang, Bowen Zhao, Mingyan Zhu +3
Partial label learning (PLL) learns from training examples each associated with multiple candidate labels, among which only one is valid. In recent years, benefiting from the stron…
Towards Effective Image Manipulation Detection with Proposal Contrastive Learning
Yuyuan Zeng, Bowen Zhao, Shanzhao Qiu +2
Deep models have been widely and successfully used in image manipulation detection, which aims to classify tampered images and localize tampered regions. Most existing methods main…
Combating Unknown Bias with Effective Bias-Conflicting Scoring and Gradient Alignment
Bowen Zhao, Chen Chen, Qian-Wei Wang +2
Models notoriously suffer from dataset biases which are detrimental to robustness and generalization. The identify-emphasize paradigm shows a promising effect in dealing with unkno…
Energy Aligning for Biased Models
Bowen Zhao, Chen Chen, Qi Ju +1
Training on class-imbalanced data usually results in biased models that tend to predict samples into the majority classes, which is a common and notorious problem. From the perspec…
Towards a category-extended object detector with limited data
Bowen Zhao, Chen Chen, Xi Xiao +1
Object detectors are typically learned on fully-annotated training data with fixed predefined categories. However, categories are often required to be increased progressively. Usua…
Maintaining Discrimination and Fairness in Class Incremental Learning
Bowen Zhao, Xi Xiao, Guojun Gan +2
Deep neural networks (DNNs) have been applied in class incremental learning, which aims to solve common real-world problems of learning new classes continually. One drawback of sta…
Self-Paced Probabilistic Principal Component Analysis for Data with Outliers
Bowen Zhao, Xi Xiao, Wanpeng Zhang +2
Principal Component Analysis (PCA) is a popular tool for dimensionality reduction and feature extraction in data analysis. There is a probabilistic version of PCA, known as Probabi…