Temporal Action Localization aims to detect activities in the video stream and output beginning and end timestamps. It is closely related to Temporal Action Proposal Generation.
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The AVA dataset densely annotates 80 atomic visual actions in 430 15-minute video clips, where actions are localized in space and time, resulting in 1. 58M action labels with multiple labels per person occurring frequently.
To address these difficulties, we introduce the Boundary-Matching (BM) mechanism to evaluate confidence scores of densely distributed proposals, which denote a proposal as a matching pair of starting and ending boundaries and combine all densely distributed BM pairs into the BM confidence map.
Temporal action proposal generation is an important yet challenging problem, since temporal proposals with rich action content are indispensable for analysing real-world videos with long duration and high proportion irrelevant content.
#2 best model for Temporal Action Localization on ActivityNet-1.3
Second, frame-based models perform quite well on action recognition; is pre-training for good image features sufficient or is pre-training for spatio-temporal features valuable for optimal transfer learning?
#2 best model for Action Classification on Kinetics-400 (using extra training data)
In this paper we discuss several forms of spatiotemporal convolutions for video analysis and study their effects on action recognition.
#3 best model for Action Recognition In Videos on Sports-1M
Dynamics of human body skeletons convey significant information for human action recognition.
#2 best model for Skeleton Based Action Recognition on Varying-view RGB-D Action-Skeleton
Furthermore, based on the temporal segment networks, we won the video classification track at the ActivityNet challenge 2016 among 24 teams, which demonstrates the effectiveness of TSN and the proposed good practices.
#5 best model for Action Classification on Moments in Time (Top 5 Accuracy metric)
The other contribution is our study on a series of good practices in learning ConvNets on video data with the help of temporal segment network.
#3 best model for Multimodal Activity Recognition on EV-Action
Over the last decade, Convolutional Neural Network (CNN) models have been highly successful in solving complex vision problems.