16 citations · 43 across the 11 of their papers we have counts for
20 papers
SMAUG: Sparse Masked Autoencoder for Efficient Video-Language Pre-training
Yuanze Lin, Chen Wei, Huiyu Wang +2
Video-language pre-training is crucial for learning powerful multi-modal representation. However, it typically requires a massive amount of computation. In this paper, we develop S…
Finding Differences Between Transformers and ConvNets Using Counterfactual Simulation Testing
Nataniel Ruiz, Sarah Adel Bargal, Cihang Xie +2
Modern deep neural networks tend to be evaluated on static test sets. One shortcoming of this is the fact that these deep neural networks cannot be easily evaluated for robustness…
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…
Fast AdvProp
Jieru Mei, Yucheng Han, Yutong Bai +5
Adversarial Propagation (AdvProp) is an effective way to improve recognition models, leveraging adversarial examples. Nonetheless, AdvProp suffers from the extremely slow training…
Calibrating Concepts and Operations: Towards Symbolic Reasoning on Real Images
Zhuowan Li, Elias Stengel-Eskin, Yixiao Zhang +4
While neural symbolic methods demonstrate impressive performance in visual question answering on synthetic images, their performance suffers on real images. We identify that the lo…
Robust and Accurate Object Detection via Adversarial Learning
Xiangning Chen, Cihang Xie, Mingxing Tan +3
Data augmentation has become a de facto component for training high-performance deep image classifiers, but its potential is under-explored for object detection. Noting that most s…