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Action recognition is a crucial task for video understanding. In this paper, we present AutoVideo, a Python system for automated video action recognition. It currently supports seven action recognition algorithms and various pre-processing modules. Unlike the existing libraries that only provide model zoos, AutoVideo is built with the standard pipeline language. The basic building block is primitive, which wraps a pre-processing module or an algorithm with some hyperparameters. AutoVideo is highly modular and extendable. It can be easily combined with AutoML searchers. The pipeline language is quite general so that we can easily enrich AutoVideo with algorithms for various other video-related tasks in the future. AutoVideo is released under MIT license at https://github.com/datamllab/autovideo
From just a short glance at a video, we can often tell whether a persons action is intentional or not. Can we train a model to recognize this? We introduce a dataset of in-the-wild videos of unintentional action, as well as a suite of tasks for recog
Video action recognition, which is topical in computer vision and video analysis, aims to allocate a short video clip to a pre-defined category such as brushing hair or climbing stairs. Recent works focus on action recognition with deep neural networ
Training robust deep video representations has proven to be much more challenging than learning deep image representations. This is in part due to the enormous size of raw video streams and the high temporal redundancy; the true and interesting signa
To overcome the limitations of convolutional neural network in the process of facial expression recognition, a facial expression recognition model Capsule-LSTM based on video frame sequence is proposed. This model is composed of three networks includ
Inspired by the observation that humans are able to process videos efficiently by only paying attention where and when it is needed, we propose an interpretable and easy plug-in spatial-temporal attention mechanism for video action recognition. For s