Temporal Action Localization
239 papers with code • 8 benchmarks • 37 datasets
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.
Benchmarks
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UCF101
Kinetics
HMDB51
MPII
ActivityNet
Charades
KTH
THUMOS14
JHMDB
UTD-MHAD
Subtasks
Greatest papers with code
VATT: Transformers for Multimodal Self-Supervised Learning from Raw Video, Audio and Text
We show that the convolution-free VATT outperforms state-of-the-art ConvNet-based architectures in the downstream tasks.
Ranked #1 on
Action Classification
on Moments in Time
(using extra training data)
MoViNets: Mobile Video Networks for Efficient Video Recognition
tensorflow/models
•
•
CVPR 2021
We present Mobile Video Networks (MoViNets), a family of computation and memory efficient video networks that can operate on streaming video for online inference.
Ranked #1 on
Action Recognition
on EPIC-KITCHENS-100
AVA: A Video Dataset of Spatio-temporally Localized Atomic Visual Actions
tensorflow/models
•
•
CVPR 2018
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.
Ranked #2 on
Temporal Action Localization
on J-HMDB-21
Large-scale weakly-supervised pre-training for video action recognition
microsoft/computervision-recipes
•
•
CVPR 2019
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?
Ranked #1 on
Egocentric Activity Recognition
on EPIC-KITCHENS-55
(Actions Top-1 (S2) metric)
A Closer Look at Spatiotemporal Convolutions for Action Recognition
microsoft/computervision-recipes
•
•
CVPR 2018
In this paper we discuss several forms of spatiotemporal convolutions for video analysis and study their effects on action recognition.
Ranked #4 on
Action Recognition
on Sports-1M
BMN: Boundary-Matching Network for Temporal Action Proposal Generation
PaddlePaddle/models
•
•
ICCV 2019
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.
Ranked #1 on
Action Recognition
on THUMOS’14
BSN: Boundary Sensitive Network for Temporal Action Proposal Generation
PaddlePaddle/models
•
•
ECCV 2018
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.
Ranked #1 on
Temporal Action Proposal Generation
on THUMOS' 14
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.
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
Dynamics of human body skeletons convey significant information for human action recognition.
Ranked #2 on
Action Recognition
on ICVL-4
Hide-and-Seek: A Data Augmentation Technique for Weakly-Supervised Localization and Beyond
Our approach only needs to modify the input image and can work with any network to improve its performance.
