Temporal Action Localization
422 papers with code • 14 benchmarks • 42 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
These leaderboards are used to track progress in Temporal Action Localization
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Libraries
Use these libraries to find Temporal Action Localization models and implementationsDatasets
UCF101
Kinetics
HMDB51
ActivityNet
MPII
Charades
THUMOS14
KTH
EPIC-KITCHENS-100
Kinetics-700
Subtasks
Latest papers with no code
Language Model Guided Interpretable Video Action Reasoning
Extensive experiments on two complex video action datasets, Charades & CAD-120, validates the improved performance and interpretability of our LaIAR framework.
LoSA: Long-Short-range Adapter for Scaling End-to-End Temporal Action Localization
Temporal Action Localization (TAL) involves localizing and classifying action snippets in an untrimmed video.
PLOT-TAL -- Prompt Learning with Optimal Transport for Few-Shot Temporal Action Localization
This paper introduces a novel approach to temporal action localization (TAL) in few-shot learning.
Boosting Semi-Supervised Temporal Action Localization by Learning from Non-Target Classes
To this end, we first devise innovative strategies to adaptively select high-quality positive and negative classes from the label space, by modeling both the confidence and rank of a class in relation to those of the target class.
Leveraging Foundation Model Automatic Data Augmentation Strategies and Skeletal Points for Hands Action Recognition in Industrial Assembly Lines
We proposed a method of converting hand action recognition problems into hand skeletal trajectory classification problems, which solved the real-time performance problem of industrial algorithms.
BID: Boundary-Interior Decoding for Unsupervised Temporal Action Localization Pre-Trainin
Skeleton-based motion representations are robust for action localization and understanding for their invariance to perspective, lighting, and occlusion, compared with images.
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.
Density-Guided Label Smoothing for Temporal Localization of Driving Actions
Temporal localization of driving actions plays a crucial role in advanced driver-assistance systems and naturalistic driving studies.
Transformer-based Fusion of 2D-pose and Spatio-temporal Embeddings for Distracted Driver Action Recognition
The model uses 2D-pose features as the positional embedding of the transformer architecture and spatio-temporal features as the main input to the encoder of the transformer.
TikTokActions: A TikTok-Derived Video Dataset for Human Action Recognition
We find that the performance of the model pre-trained using our Tik-Tok dataset is comparable to models trained on larger action recognition datasets (95. 3% on UCF101 and 53. 24% on HMDB51).
