179 citations · 292 across the 17 of their papers we have counts for
5 papers · 1 filter
MVEB: Self-Supervised Learning with Multi-View Entropy Bottleneck
Liangjian Wen, Xiasi Wang, Jianzhuang Liu +1
Self-supervised learning aims to learn representation that can be effectively generalized to downstream tasks. Many self-supervised approaches regard two views of an image as both…
Self-Supervision Can Be a Good Few-Shot Learner
Yuning Lu, Liangjian Wen, Jianzhuang Liu +2
Existing few-shot learning (FSL) methods rely on training with a large labeled dataset, which prevents them from leveraging abundant unlabeled data. From an information-theoretic p…
Boosting Few-Shot Classification with View-Learnable Contrastive Learning
Xu Luo, Yuxuan Chen, Liangjian Wen +2
The goal of few-shot classification is to classify new categories with few labeled examples within each class. Nowadays, the excellent performance in handling few-shot classificati…
Rectifying the Shortcut Learning of Background for Few-Shot Learning
Xu Luo, Longhui Wei, Liangjian Wen +4
The category gap between training and evaluation has been characterised as one of the main obstacles to the success of Few-Shot Learning (FSL). In this paper, we for the first time…
AFINet: Attentive Feature Integration Networks for Image Classification
Xinglin Pan, Jing Xu, Yu Pan +4
Convolutional Neural Networks (CNNs) have achieved tremendous success in a number of learning tasks including image classification. Recent advanced models in CNNs, such as ResNets,…