Weakly-supervised Temporal Action Localization
32 papers with code • 2 benchmarks • 2 datasets
Temporal Action Localization with weak supervision where only video-level labels are given for training
Libraries
Use these libraries to find Weakly-supervised Temporal Action Localization models and implementations
THUMOS14
UCF101-24
Most implemented papers
Weakly Supervised Action Localization by Sparse Temporal Pooling Network
We propose a weakly supervised temporal action localization algorithm on untrimmed videos using convolutional neural networks.
Background Suppression Network for Weakly-supervised Temporal Action Localization
This formulation does not fully model the problem in that background frames are forced to be misclassified as action classes to predict video-level labels accurately.
Weakly-supervised Temporal Action Localization by Uncertainty Modeling
Pilhyeon/WTAL-Uncertainty-Modeling
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Experimental results show that our uncertainty modeling is effective at alleviating the interference of background frames and brings a large performance gain without bells and whistles.
ACM-Net: Action Context Modeling Network for Weakly-Supervised Temporal Action Localization
ispc-lab/ACM-Net
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Traditional methods mainly focus on foreground and background frames separation with only a single attention branch and class activation sequence.
Cross-modal Consensus Network for Weakly Supervised Temporal Action Localization
In this work, we argue that the features extracted from the pretrained extractor, e. g., I3D, are not the WS-TALtask-specific features, thus the feature re-calibration is needed for reducing the task-irrelevant information redundancy.
AutoLoc: Weakly-supervised Temporal Action Localization
In this paper, we first develop a novel weakly-supervised TAL framework called AutoLoc to directly predict the temporal boundary of each action instance.
RefineLoc: Iterative Refinement for Weakly-Supervised Action Localization
RefineLoc shows competitive results with the state-of-the-art in weakly-supervised temporal localization.
Completeness Modeling and Context Separation for Weakly Supervised Temporal Action Localization
Finspire13/CMCS-Temporal-Action-Localization
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CVPR 2019
In this work, we first identify two underexplored problems posed by the weak supervision for temporal action localization, namely action completeness modeling and action-context separation.
3C-Net: Category Count and Center Loss for Weakly-Supervised Action Localization
Our joint formulation has three terms: a classification term to ensure the separability of learned action features, an adapted multi-label center loss term to enhance the action feature discriminability and a counting loss term to delineate adjacent action sequences, leading to improved localization.
Weakly Supervised Temporal Action Localization Using Deep Metric Learning
We propose a classification module to generate action labels for each segment in the video, and a deep metric learning module to learn the similarity between different action instances.
