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Image Classification Based on Few-Shot Learning

38 minute read


This blog classifies and summarizes the current image classification algorithms based on Few-shot learning. According to the modeling methods of different data types, Few-shot image classification algorithms are divided into convolution neural network model and graph neural network model. Convolution neural network model is divided into four learning paradigms: transfer learning, meta learning, Bayesian learning and dual learning. Two kinds of algorithms are introduced in detail. Finally, for the current Few-shot image classification algorithm, we discuss the ethical oriented implications in terms of interpretability and privacy.

Temporal action localization with Semi-Supervised Learning

8 minute read


Temporal action localization is an important task in the field of video content analysis, and its goal is to locate the action instances with precise boundaries and flexible intervals in an untrimmed video. Constructing a large enough data set that contains video clips and annotations will require vast human effort. Since there are quite many unlabeled videos on the internet, it is worth trying to combine semi-supervised approaches into temporal action localization task.


1 minute read



ActivityNet Challenge 2019 冠军模型BMN算法全解析

less than 1 minute read





Learning Effective Representations from Global and Local Features for Cross-View Gait Recognition

Published in arXiv preprint, 2020

Gait recognition is one of the most important biometric technologies and has been applied in many fields. Recent gait recognition frameworks represent each human gait frame by descriptors extracted from either global appearances or local regions of humans. However, the representations based on global information often neglect the details of the gait frame, while local region based descriptors cannot capture the relations among neighboring regions, thus reducing their discriminativeness. In this paper, we propose a novel feature extraction and fusion framework to achieve discriminative feature representations for gait recognition. Towards this goal, we take advantage of both global visual information and local region details and develop a Global and Local Feature Extractor (GLFE). Specifically, our GLFE module is composed of our newly designed multiple global and local convolutional layers (GLConv) to ensemble global and local features in a principle manner. Furthermore, we present a novel operation, namely Local Temporal Aggregation (LTA), to further preserve the spatial information by reducing the temporal resolution to obtain higher spatial resolution. With the help of our GLFE and LTA, our method significantly improves the discriminativeness of our visual features, thus improving the gait recognition performance. Extensive experiments demonstrate that our proposed method outperforms state-of-the-art gait recognition methods on popular widely-used CASIA-B and OUMVLP datasets.

Recommended citation: Lin, Beibei, Shunli Zhang, Xin Yu, Zedong Chu, and Haikun Zhang. "Learning Effective Representations from Global and Local Features for Cross-View Gait Recognition." arXiv preprint arXiv:2011.01461 (2020).



Teaching experience 1

Undergraduate course, University 1, Department, 2014

This is a description of a teaching experience. You can use markdown like any other post.

Teaching experience 2

Workshop, University 1, Department, 2015

This is a description of a teaching experience. You can use markdown like any other post.