794 citations · 1.1k across the 7 of their papers we have counts for
9 papers
LUMix: Improving Mixup by Better Modelling Label Uncertainty
Shuyang Sun, Jie-Neng Chen, Ruifei He +3
Modern deep networks can be better generalized when trained with noisy samples and regularization techniques. Mixup and CutMix have been proven to be effective for data augmentatio…
Knowledge Distillation as Efficient Pre-training: Faster Convergence, Higher Data-efficiency, and Better Transferability
Ruifei He, Shuyang Sun, Jihan Yang +2
Large-scale pre-training has been proven to be crucial for various computer vision tasks. However, with the increase of pre-training data amount, model architecture amount, and the…
Vision Transformer with Progressive Sampling
Xiaoyu Yue, Shuyang Sun, Zhanghui Kuang +4
Transformers with powerful global relation modeling abilities have been introduced to fundamental computer vision tasks recently. As a typical example, the Vision Transformer (ViT)…
Learning to Sample the Most Useful Training Patches from Images
Shuyang Sun, Liang Chen, Gregory Slabaugh +1
Some image restoration tasks like demosaicing require difficult training samples to learn effective models. Existing methods attempt to address this data training problem by manual…
Exploring the Hierarchy in Relation Labels for Scene Graph Generation
Yi Zhou, Shuyang Sun, Chao Zhang +2
By assigning each relationship a single label, current approaches formulate the relationship detection as a classification problem. Under this formulation, predicate categories are…
Robust Multi-Modality Multi-Object Tracking
Wenwei Zhang, Hui Zhou, Shuyang Sun +3
Multi-sensor perception is crucial to ensure the reliability and accuracy in autonomous driving system, while multi-object tracking (MOT) improves that by tracing sequential moveme…