90 citations · 143 across the 6 of their papers we have counts for
8 papers
The Two Dimensions of Worst-case Training and the Integrated Effect for Out-of-domain Generalization
Zeyi Huang, Haohan Wang, Dong Huang +2
Training with an emphasis on "hard-to-learn" components of the data has been proven as an effective method to improve the generalization of machine learning models, especially in t…
Not All Images are Worth 16x16 Words: Dynamic Transformers for Efficient Image Recognition
Yulin Wang, Rui Huang, Shiji Song +2
Vision Transformers (ViT) have achieved remarkable success in large-scale image recognition. They split every 2D image into a fixed number of patches, each of which is treated as a…
Squared Norm as Consistency Loss for Leveraging Augmented Data to Learn Robust and Invariant Representations
Haohan Wang, Zeyi Huang, Xindi Wu +1
Data augmentation is one of the most popular techniques for improving the robustness of neural networks. In addition to directly training the model with original samples and augmen…
Comprehensive Attention Self-Distillation for Weakly-Supervised Object Detection
Zeyi Huang, Yang Zou, Vijayakumar Bhagavatula +1
Weakly Supervised Object Detection (WSOD) has emerged as an effective tool to train object detectors using only the image-level category labels. However, without object-level label…
Self-Challenging Improves Cross-Domain Generalization
Zeyi Huang, Haohan Wang, Eric P. Xing +1
Convolutional Neural Networks (CNN) conduct image classification by activating dominant features that correlated with labels. When the training and testing data are under similar d…
Multiple Anchor Learning for Visual Object Detection
Wei Ke, Tianliang Zhang, Zeyi Huang +3
Classification and localization are two pillars of visual object detectors. However, in CNN-based detectors, these two modules are usually optimized under a fixed set of candidate…