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
Latest papers with no code
Simba: Mamba augmented U-ShiftGCN for Skeletal Action Recognition in Videos
These spatial features then undergo intermediate temporal modeling facilitated by the Mamba block before progressing to the encoder section, which comprises vanilla upsampling Shift S-GCN blocks.
Deep Learning Approaches for Human Action Recognition in Video Data
The results of this study underscore the potential of composite models in achieving robust human action recognition and suggest avenues for future research in optimizing these models for real-world deployment.
DVANet: Disentangling View and Action Features for Multi-View Action Recognition
In this work, we present a novel approach to multi-view action recognition where we guide learned action representations to be separated from view-relevant information in a video.
Action Class Relation Detection and Classification Across Multiple Video Datasets
The Meta Video Dataset (MetaVD) provides annotated relations between action classes in major datasets for human action recognition in videos.
Knowledge Prompting for Few-shot Action Recognition
Few-shot action recognition in videos is challenging for its lack of supervision and difficulty in generalizing to unseen actions.
Could Giant Pretrained Image Models Extract Universal Representations?
In this paper, we present a study of frozen pretrained models when applied to diverse and representative computer vision tasks, including object detection, semantic segmentation and video action recognition.
Class-Incremental Learning for Action Recognition in Videos
We tackle catastrophic forgetting problem in the context of class-incremental learning for video recognition, which has not been explored actively despite the popularity of continual learning.
Co-training Transformer with Videos and Images Improves Action Recognition
We term this approach as Co-training Videos and Images for Action Recognition (CoVeR).
Technical Report: Disentangled Action Parsing Networks for Accurate Part-level Action Parsing
Despite of dramatic progresses in the area of video classification research, a severe problem faced by the community is that the detailed understanding of human actions is ignored.
Class incremental learning for video action classification
Class Incremental Learning (CIL) is a hot topic in machine learning for CNN models to learn new classes incrementally.
