539 citations · 2k across the 53 of their papers we have counts for
10 papers · 2 filters
A Unified Pruning Framework for Vision Transformers
Hao Yu, Jianxin Wu
Recently, vision transformer (ViT) and its variants have achieved promising performances in various computer vision tasks. Yet the high computational costs and training data requir…
Fine-Grained Image Analysis with Deep Learning: A Survey
Xiu-Shen Wei, Yi-Zhe Song, Oisin Mac Aodha +5
Fine-grained image analysis (FGIA) is a longstanding and fundamental problem in computer vision and pattern recognition, and underpins a diverse set of real-world applications. The…
Residual Attention: A Simple but Effective Method for Multi-Label Recognition
Ke Zhu, Jianxin Wu
Multi-label image recognition is a challenging computer vision task of practical use. Progresses in this area, however, are often characterized by complicated methods, heavy comput…
Webly Supervised Fine-Grained Recognition: Benchmark Datasets and An Approach
Zeren Sun, Yazhou Yao, Xiu-Shen Wei +5
Learning from the web can ease the extreme dependence of deep learning on large-scale manually labeled datasets. Especially for fine-grained recognition, which targets at distingui…
Weakly Supervised Foreground Learning for Weakly Supervised Localization and Detection
Chen-Lin Zhang, Yin Li, Jianxin Wu
Modern deep learning models require large amounts of accurately annotated data, which is often difficult to satisfy. Hence, weakly supervised tasks, including weakly supervised obj…
A Random CNN Sees Objects: One Inductive Bias of CNN and Its Applications
Yun-Hao Cao, Jianxin Wu
This paper starts by revealing a surprising finding: without any learning, a randomly initialized CNN can localize objects surprisingly well. That is, a CNN has an inductive bias t…
Friends and Foes in Learning from Noisy Labels
Yifan Zhou, Yifan Ge, Jianxin Wu
Learning from examples with noisy labels has attracted increasing attention recently. But, this paper will show that the commonly used CIFAR-based datasets and the accuracy evaluat…
Rethinking Self-Supervised Learning: Small is Beautiful
Yun-Hao Cao, Jianxin Wu
Self-supervised learning (SSL), in particular contrastive learning, has made great progress in recent years. However, a common theme in these methods is that they inherit the learn…
Distilling Virtual Examples for Long-tailed Recognition
Yin-Yin He, Jianxin Wu, Xiu-Shen Wei
We tackle the long-tailed visual recognition problem from the knowledge distillation perspective by proposing a Distill the Virtual Examples (DiVE) method. Specifically, by treatin…
Mixup Without Hesitation
Hao Yu, Huanyu Wang, Jianxin Wu
Mixup linearly interpolates pairs of examples to form new samples, which is easy to implement and has been shown to be effective in image classification tasks. However, there are t…