401 citations · 1.6k across the 64 of their papers we have counts for
14 papers · 1 filter
Unleashing the Power of Visual Prompting At the Pixel Level
Junyang Wu, Xianhang Li, Chen Wei +4
This paper presents a simple and effective visual prompting method for adapting pre-trained models to downstream recognition tasks. Our method includes two key designs. First, rath…
Localization vs. Semantics: Visual Representations in Unimodal and Multimodal Models
Zhuowan Li, Cihang Xie, Benjamin Van Durme +1
Despite the impressive advancements achieved through vision-and-language pretraining, it remains unclear whether this joint learning paradigm can help understand each individual mo…
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…
Navigation as Attackers Wish? Towards Building Robust Embodied Agents under Federated Learning
Yunchao Zhang, Zonglin Di, Kaiwen Zhou +2
Federated embodied agent learning protects the data privacy of individual visual environments by keeping data locally at each client (the individual environment) during training. H…
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…
A Simple Data Mixing Prior for Improving Self-Supervised Learning
Sucheng Ren, Huiyu Wang, Zhengqi Gao +4
Data mixing (e.g., Mixup, Cutmix, ResizeMix) is an essential component for advancing recognition models. In this paper, we focus on studying its effectiveness in the self-supervise…
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…
One-Pixel Shortcut: on the Learning Preference of Deep Neural Networks
Shutong Wu, Sizhe Chen, Cihang Xie +1
Unlearnable examples (ULEs) aim to protect data from unauthorized usage for training DNNs. Existing work adds -bounded perturbations to the original sample so that the…
Adversarial Attack on Attackers: Post-Process to Mitigate Black-Box Score-Based Query Attacks
Sizhe Chen, Zhehao Huang, Qinghua Tao +3
The score-based query attacks (SQAs) pose practical threats to deep neural networks by crafting adversarial perturbations within dozens of queries, only using the model's output sc…
In Defense of Image Pre-Training for Spatiotemporal Recognition
Xianhang Li, Huiyu Wang, Chen Wei +4
Image pre-training, the current de-facto paradigm for a wide range of visual tasks, is generally less favored in the field of video recognition. By contrast, a common strategy is t…
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…
L2B: Learning to Bootstrap Robust Models for Combating Label Noise
Yuyin Zhou, Xianhang Li, Fengze Liu +6
Deep neural networks have shown great success in representation learning. However, when learning with noisy labels (LNL), they can easily overfit and fail to generalize to new data…