50 citations · 79 across the 17 of their papers we have counts for
12 papers
ShuffleMix: Improving Representations via Channel-Wise Shuffle of Interpolated Hidden States
Kangjun Liu, Ke Chen, Lihua Guo +2
Mixup style data augmentation algorithms have been widely adopted in various tasks as implicit network regularization on representation learning to improve model generalization, wh…
STFAR: Improving Object Detection Robustness at Test-Time by Self-Training with Feature Alignment Regularization
Yijin Chen, Xun Xu, Yongyi Su +1
Domain adaptation helps generalizing object detection models to target domain data with distribution shift. It is often achieved by adapting with access to the whole target domain…
Revisiting Realistic Test-Time Training: Sequential Inference and Adaptation by Anchored Clustering Regularized Self-Training
Yongyi Su, Xun Xu, Tianrui Li +1
Deploying models on target domain data subject to distribution shift requires adaptation. Test-time training (TTT) emerges as a solution to this adaptation under a realistic scenar…
A New Benchmark: On the Utility of Synthetic Data with Blender for Bare Supervised Learning and Downstream Domain Adaptation
Hui Tang, Kui Jia
Deep learning in computer vision has achieved great success with the price of large-scale labeled training data. However, exhaustive data annotation is impracticable for each task…
HelixSurf: A Robust and Efficient Neural Implicit Surface Learning of Indoor Scenes with Iterative Intertwined Regularization
Zhihao Liang, Zhangjin Huang, Changxing Ding +1
Recovery of an underlying scene geometry from multiview images stands as a long-time challenge in computer vision research. The recent promise leverages neural implicit surface lea…
Convolutional Fine-Grained Classification with Self-Supervised Target Relation Regularization
Kangjun Liu, Ke Chen, Kui Jia
Fine-grained visual classification can be addressed by deep representation learning under supervision of manually pre-defined targets (e.g., one-hot or the Hadamard codes). Such ta…