15 citations · 35 across the 7 of their papers we have counts for
6 papers · 1 filter
DistillBEV: Boosting Multi-Camera 3D Object Detection with Cross-Modal Knowledge Distillation
Zeyu Wang, Dingwen Li, Chenxu Luo +2
3D perception based on the representations learned from multi-camera bird's-eye-view (BEV) is trending as cameras are cost-effective for mass production in autonomous driving indus…
CLIPA-v2: Scaling CLIP Training with 81.1% Zero-shot ImageNet Accuracy within a $10,000 Budget; An Extra $4,000 Unlocks 81.8% Accuracy
Xianhang Li, Zeyu Wang, Cihang Xie
The recent work CLIPA presents an inverse scaling law for CLIP training -- whereby the larger the image/text encoders used, the shorter the sequence length of image/text tokens tha…
An Inverse Scaling Law for CLIP Training
Xianhang Li, Zeyu Wang, Cihang Xie
CLIP, one of the pioneering foundation models that connect images and text, has enabled many recent breakthroughs in computer vision. However, its associated training cost is prohi…
Bag of Tricks for FGSM Adversarial Training
Zichao Li, Li Liu, Zeyu Wang +2
Adversarial training (AT) with samples generated by Fast Gradient Sign Method (FGSM), also known as FGSM-AT, is a computationally simple method to train robust networks. However, d…
Masked Autoencoders Enable Efficient Knowledge Distillers
Yutong Bai, Zeyu Wang, Junfei Xiao +5
This paper studies the potential of distilling knowledge from pre-trained models, especially Masked Autoencoders. Our approach is simple: in addition to optimizing the pixel recons…
Can CNNs Be More Robust Than Transformers?
Zeyu Wang, Yutong Bai, Yuyin Zhou +1
The recent success of Vision Transformers is shaking the long dominance of Convolutional Neural Networks (CNNs) in image recognition for a decade. Specifically, in terms of robustn…