214 citations · 640 across the 24 of their papers we have counts for
29 papers
Bootstrap Your Object Detector via Mixed Training
Mengde Xu, Zheng Zhang, Fangyun Wei +5
We introduce MixTraining, a new training paradigm for object detection that can improve the performance of existing detectors for free. MixTraining enhances data augmentation by ut…
Generating Natural Language Adversarial Examples through An Improved Beam Search Algorithm
Tengfei Zhao, Zhaocheng Ge, Hanping Hu +1
The research of adversarial attacks in the text domain attracts many interests in the last few years, and many methods with a high attack success rate have been proposed. However,…
Semi-Supervised Semantic Segmentation via Adaptive Equalization Learning
Hanzhe Hu, Fangyun Wei, Han Hu +3
Due to the limited and even imbalanced data, semi-supervised semantic segmentation tends to have poor performance on some certain categories, e.g., tailed categories in Cityscapes…
Video Swin Transformer
Ze Liu, Jia Ning, Yue Cao +4
The vision community is witnessing a modeling shift from CNNs to Transformers, where pure Transformer architectures have attained top accuracy on the major video recognition benchm…
End-to-End Semi-Supervised Object Detection with Soft Teacher
Mengde Xu, Zheng Zhang, Han Hu +5
This paper presents an end-to-end semi-supervised object detection approach, in contrast to previous more complex multi-stage methods. The end-to-end training gradually improves ps…
Aligning Pretraining for Detection via Object-Level Contrastive Learning
Fangyun Wei, Yue Gao, Zhirong Wu +2
Image-level contrastive representation learning has proven to be highly effective as a generic model for transfer learning. Such generality for transfer learning, however, sacrific…