Action Recognition In Videos
64 papers with code • 17 benchmarks • 17 datasets
Action Recognition in Videos is a task in computer vision and pattern recognition where the goal is to identify and categorize human actions performed in a video sequence. The task involves analyzing the spatiotemporal dynamics of the actions and mapping them to a predefined set of action classes, such as running, jumping, or swimming.
Benchmarks
These leaderboards are used to track progress in Action Recognition In Videos
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Libraries
Use these libraries to find Action Recognition In Videos models and implementationsDatasets
UCF101
Kinetics
HMDB51
ActivityNet
Kinetics 400
NTU RGB+D
THUMOS14
Something-Something V2
Sports-1M
Kinetics-600
Most implemented papers
Busy-Quiet Video Disentangling for Video Classification
guoxih/Busy-Quiet-Video-Disentangling-for-Video-Classification
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We design a trainable Motion Band-Pass Module (MBPM) for separating busy information from quiet information in raw video data.
VATT: Transformers for Multimodal Self-Supervised Learning from Raw Video, Audio and Text
google-research/google-research
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NeurIPS 2021
We train VATT end-to-end from scratch using multimodal contrastive losses and evaluate its performance by the downstream tasks of video action recognition, audio event classification, image classification, and text-to-video retrieval.
ActionCLIP: A New Paradigm for Video Action Recognition
Moreover, to handle the deficiency of label texts and make use of tremendous web data, we propose a new paradigm based on this multimodal learning framework for action recognition, which we dub "pre-train, prompt and fine-tune".
Video Action Recognition Collaborative Learning with Dynamics via PSO-ConvNet Transformer
To extend our approach to video, we integrate ConvNets with state-of-the-art temporal methods such as Transformer and Recurrent Neural Networks.
Hiera: A Hierarchical Vision Transformer without the Bells-and-Whistles
Modern hierarchical vision transformers have added several vision-specific components in the pursuit of supervised classification performance.
ActNetFormer: Transformer-ResNet Hybrid Method for Semi-Supervised Action Recognition in Videos
Our framework leverages both labeled and unlabelled data to robustly learn action representations in videos, combining pseudo-labeling with contrastive learning for effective learning from both types of samples.
Convolutional Two-Stream Network Fusion for Video Action Recognition
Recent applications of Convolutional Neural Networks (ConvNets) for human action recognition in videos have proposed different solutions for incorporating the appearance and motion information.
Learning Latent Sub-events in Activity Videos Using Temporal Attention Filters
In this paper, we newly introduce the concept of temporal attention filters, and describe how they can be used for human activity recognition from videos.
Spatiotemporal Residual Networks for Video Action Recognition
Two-stream Convolutional Networks (ConvNets) have shown strong performance for human action recognition in videos.
Two-stream Flow-guided Convolutional Attention Networks for Action Recognition
This paper proposes a two-stream flow-guided convolutional attention networks for action recognition in videos.
