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Posts
Image Classification Based on Few-Shot Learning
Published:
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
Published:
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.
论文速读 - 移动计算中基于VM的Cloudlet案例
Published:
移动计算还未真正挖掘其潜力，因此文章提出一种全新的架构。
探秘计算机视觉中的注意力机制
Published:
近年来，越来越多的工作专注于将注意力机制融入计算机视觉任务中。本文重点介绍注意力机制的基本原理和利用飞桨实现注意力机制的基本方法。
ActivityNet Challenge 2019 冠军模型BMN算法全解析
Published:
随着互联网世界视频数量的急剧增长，视频内容分析技术得到了越来越广泛关注。视频动作定位是视频内容分析领域里的一个重要任务，它的目标是在一段未经修剪的视频中定位出动作类别及其对应的时序边界。通常视频动作定位任务可以分为两个阶段：时序提名生成和动作分类。本文所介绍的算法BMN为百度自研，为时序提名生成任务提供了一个高效的解决方案，是ActivityNet2019大赛中的task1和task2的冠军方案（task1为时序提名生成，task2为视频动作定位）。
深度学习 训练集与验证集损失分析
Published:
深度学习中，如何对训练集与验证集上的Loss变化进行正确的分析，并进行妥当的改善？
portfolio
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publications
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). https://arxiv.org/abs/2011.01461
talks
Talk 1 on Relevant Topic in Your Field
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This is a description of your talk, which is a markdown files that can be all markdown-ified like any other post. Yay markdown!
Conference Proceeding talk 3 on Relevant Topic in Your Field
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This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
teaching
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.