107 citations · 218 across the 10 of their papers we have counts for
10 papers
Task-Specific Distance Correlation Matching for Few-Shot Action Recognition
Fei Long, Yao Zhang, Jiaming Lv +2
Few-shot action recognition (FSAR) has recently made notable progress through set matching and efficient adaptation of large-scale pre-trained models. However, two key limitations…
DALIP: Distribution Alignment-based Language-Image Pre-Training for Domain-Specific Data
Junjie Wu, Jiangtao Xie, Zhaolin Zhang +4
Recently, Contrastive Language-Image Pre-training (CLIP) has shown promising performance in domain-specific data (e.g., biology), and has attracted increasing research attention. E…
Joint Distribution Matters: Deep Brownian Distance Covariance for Few-Shot Classification
Jiangtao Xie, Fei Long, Jiaming Lv +2
Few-shot classification is a challenging problem as only very few training examples are given for each new task. One of the effective research lines to address this challenge focus…
Binocular Mutual Learning for Improving Few-shot Classification
Ziqi Zhou, Xi Qiu, Jiangtao Xie +2
Most of the few-shot learning methods learn to transfer knowledge from datasets with abundant labeled data (i.e., the base set). From the perspective of class space on base set, ex…
3rd Place Solution for Short-video Face Parsing Challenge
Xiao Liu, Xiaofei Si, Jiangtao Xie
This is a short technical report introducing the solution of Team Rat for Short-video Parsing Face Parsing Track of The 3rd Person in Context (PIC) Workshop and Challenge at CVPR 2…
SoT: Delving Deeper into Classification Head for Transformer
Jiangtao Xie, Ruiren Zeng, Qilong Wang +2
Transformer models are not only successful in natural language processing (NLP) but also demonstrate high potential in computer vision (CV). Despite great advance, most of works on…
Deep CNNs Meet Global Covariance Pooling: Better Representation and Generalization
Qilong Wang, Jiangtao Xie, Wangmeng Zuo +2
Compared with global average pooling in existing deep convolutional neural networks (CNNs), global covariance pooling can capture richer statistics of deep features, having potenti…
Global Second-order Pooling Convolutional Networks
Zilin Gao, Jiangtao Xie, Qilong Wang +1
Deep Convolutional Networks (ConvNets) are fundamental to, besides large-scale visual recognition, a lot of vision tasks. As the primary goal of the ConvNets is to characterize com…
Towards Faster Training of Global Covariance Pooling Networks by Iterative Matrix Square Root Normalization
Peihua Li, Jiangtao Xie, Qilong Wang +1
Global covariance pooling in convolutional neural networks has achieved impressive improvement over the classical first-order pooling. Recent works have shown matrix square root no…
Is Second-order Information Helpful for Large-scale Visual Recognition?
Peihua Li, Jiangtao Xie, Qilong Wang +1
By stacking layers of convolution and nonlinearity, convolutional networks (ConvNets) effectively learn from low-level to high-level features and discriminative representations. Si…