Weakly Supervised Temporal Action Localization
31 papers with code • 1 benchmarks • 2 datasets
Libraries
Use these libraries to find Weakly Supervised Temporal Action Localization models and implementations
THUMOS14
FineAction
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.
