# Weakly Supervised Temporal Action Localization

23 papers with code • 1 benchmarks • 2 datasets

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## Libraries

Use these libraries to find Weakly Supervised Temporal Action Localization models and implementations
3 papers
60

# 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.

3

# Background Suppression Network for Weakly-supervised Temporal Action Localization

22 Nov 2019

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.

2

# Weakly-supervised Temporal Action Localization by Uncertainty Modeling

12 Jun 2020

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.

2

# ACM-Net: Action Context Modeling Network for Weakly-Supervised Temporal Action Localization

7 Apr 2021

Traditional methods mainly focus on foreground and background frames separation with only a single attention branch and class activation sequence.

2

# AutoLoc: Weakly-supervised Temporal Action Localization

22 Jul 2018

In this paper, we first develop a novel weakly-supervised TAL framework called AutoLoc to directly predict the temporal boundary of each action instance.

1

# RefineLoc: Iterative Refinement for Weakly-Supervised Action Localization

30 Mar 2019

RefineLoc shows competitive results with the state-of-the-art in weakly-supervised temporal localization.

1

# Completeness Modeling and Context Separation for Weakly Supervised Temporal Action Localization

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.

1

# 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.

1

# Weakly Supervised Temporal Action Localization Using Deep Metric Learning

21 Jan 2020

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.

1

# Weakly-Supervised Action Localization by Generative Attention Modeling

By maximizing the conditional probability with respect to the attention, the action and non-action frames are well separated.

1