Blog posts


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