903 citations · 2.5k across the 78 of their papers we have counts for
18 papers · 1 filter
Alleviating the Sample Selection Bias in Few-shot Learning by Removing Projection to the Centroid
Jing Xu, Xu Luo, Xinglin Pan +3
Few-shot learning (FSL) targets at generalization of vision models towards unseen tasks without sufficient annotations. Despite the emergence of a number of few-shot learning metho…
ViLPAct: A Benchmark for Compositional Generalization on Multimodal Human Activities
Terry Yue Zhuo, Yaqing Liao, Yuecheng Lei +5
We introduce ViLPAct, a novel vision-language benchmark for human activity planning. It is designed for a task where embodied AI agents can reason and forecast future actions of hu…
Channel Importance Matters in Few-Shot Image Classification
Xu Luo, Jing Xu, Zenglin Xu
Few-Shot Learning (FSL) requires vision models to quickly adapt to brand-new classification tasks with a shift in task distribution. Understanding the difficulties posed by this ta…
Semantically Proportional Patchmix for Few-Shot Learning
Jingquan Wang, Jing Xu, Yu Pan +1
Few-shot learning aims to classify unseen classes with only a limited number of labeled data. Recent works have demonstrated that training models with a simple transfer learning st…
Exploring Category-correlated Feature for Few-shot Image Classification
Jing Xu, Xinglin Pan, Xu Luo +2
Few-shot classification aims to adapt classifiers to novel classes with a few training samples. However, the insufficiency of training data may cause a biased estimation of feature…
Self-Paced Deep Regression Forests with Consideration of Ranking Fairness
Lili Pan, Mingming Meng, Yazhou Ren +2
Deep discriminative models (DDMs), e.g. deep regression forests and deep decision forests, have been extensively studied recently to solve problems such as facial age estimation, h…
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,…
Pseudo-supervised Deep Subspace Clustering
Juncheng Lv, Zhao Kang, Xiao Lu +1
Auto-Encoder (AE)-based deep subspace clustering (DSC) methods have achieved impressive performance due to the powerful representation extracted using deep neural networks while pr…
MultiFace: A Generic Training Mechanism for Boosting Face Recognition Performance
Jing Xu, Tszhang Guo, Yong Xu +2
Deep Convolutional Neural Networks (DCNNs) and their variants have been widely used in large scale face recognition(FR) recently. Existing methods have achieved good performance on…
Heuristic Rank Selection with Progressively Searching Tensor Ring Network
Nannan Li, Yu Pan, Yaran Chen +3
Recently, Tensor Ring Networks (TRNs) have been applied in deep networks, achieving remarkable successes in compression ratio and accuracy. Although highly related to the performan…
Angular Triplet Loss-based Camera Network for ReID
Yitian Li, Ruini Xue, Mengmeng Zhu +2
Person re-identification (ReID) is a challenging crosscamera retrieval task to identify pedestrians. Many complex network structures are proposed recently and many of them concentr…
Self-Paced Deep Regression Forests with Consideration on Underrepresented Examples
Lili Pan, Shijie Ai, Yazhou Ren +1
Deep discriminative models (e.g. deep regression forests, deep neural decision forests) have achieved remarkable success recently to solve problems such as facial age estimation an…
DART: Domain-Adversarial Residual-Transfer Networks for Unsupervised Cross-Domain Image Classification
Xianghong Fang, Haoli Bai, Ziyi Guo +3
The accuracy of deep learning (e.g., convolutional neural networks) for an image classification task critically relies on the amount of labeled training data. Aiming to solve an im…
Robust Graph Learning from Noisy Data
Zhao Kang, Haiqi Pan, Steven C. H. Hoi +1
Learning graphs from data automatically has shown encouraging performance on clustering and semisupervised learning tasks. However, real data are often corrupted, which may cause t…
Compressing Recurrent Neural Networks with Tensor Ring for Action Recognition
Yu Pan, Jing Xu, Maolin Wang +4
Recurrent Neural Networks (RNNs) and their variants, such as Long-Short Term Memory (LSTM) networks, and Gated Recurrent Unit (GRU) networks, have achieved promising performance in…
Two Birds with One Stone: Transforming and Generating Facial Images with Iterative GAN
Dan Ma, Bin Liu, Zhao Kang +3
Generating high fidelity identity-preserving faces with different facial attributes has a wide range of applications. Although a number of generative models have been developed to…