Temporal Action Localization
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.
Leaderboards
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Subtasks
Greatest papers with code
AVA: A Video Dataset of Spatio-temporally Localized Atomic Visual Actions
CVPR 2018
• tensorflow/models
• 
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.
BMN: Boundary-Matching Network for Temporal Action Proposal Generation
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.
BSN: Boundary Sensitive Network for Temporal Action Proposal Generation
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
Large-scale weakly-supervised pre-training for video action recognition
CVPR 2019
• microsoft/computervision-recipes
• 
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)
ACTION CLASSIFICATION ACTION RECOGNITION IN VIDEOS ACTIVITY RECOGNITION IN VIDEOS TRANSFER LEARNING
A Closer Look at Spatiotemporal Convolutions for Action Recognition
CVPR 2018
• microsoft/computervision-recipes
•
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
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
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
3D HUMAN POSE ESTIMATION ACTION RECOGNITION IN VIDEOS MULTIMODAL ACTIVITY RECOGNITION SKELETON BASED ACTION RECOGNITION
Temporal Segment Networks for Action Recognition in Videos
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)
Temporal Segment Networks: Towards Good Practices for Deep Action Recognition
• yjxiong/temporal-segment-networks
•
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
ACTION RECOGNITION IN VIDEOS MULTIMODAL ACTIVITY RECOGNITION
Sparse 3D convolutional neural networks
• facebookresearch/SparseConvNet
•
We have implemented a convolutional neural network designed for processing sparse three-dimensional input data.
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
Over the last decade, Convolutional Neural Network (CNN) models have been highly successful in solving complex vision problems.