Weakly-supervised Temporal Action Localization

13 papers with code • 1 benchmarks • 1 datasets

Temporal Action Localization with weak supervision where only video-level labels are given for training

Datasets


Greatest papers with code

Background Suppression Network for Weakly-supervised Temporal Action Localization

Pilhyeon/BaSNet-pytorch 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.

Weakly Supervised Action Localization Weakly-supervised Temporal Action Localization +1

Weakly-supervised Temporal Action Localization by Uncertainty Modeling

Pilhyeon/WTAL-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.

Action Classification Multiple Instance Learning +4

AutoLoc: Weakly-supervised Temporal Action Localization

zhengshou/AutoLoc 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.

Weakly-supervised Temporal Action Localization Weakly Supervised Temporal Action Localization

3C-Net: Category Count and Center Loss for Weakly-Supervised Action Localization

naraysa/3c-net ICCV 2019

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.

Action Classification Weakly Supervised Action Localization +2

Adversarial Background-Aware Loss for Weakly-supervised Temporal Activity Localization

MichiganCOG/A2CL-PT ECCV 2020

Two triplets of the feature space are considered in our approach: one triplet is used to learn discriminative features for each activity class, and the other one is used to distinguish the features where no activity occurs (i. e. background features) from activity-related features for each video.

Metric Learning Weakly Supervised Action Localization +1

Weakly Supervised Temporal Action Localization Using Deep Metric Learning

asrafulashiq/wsad 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.

Metric Learning Temporal Localization +3

A Hybrid Attention Mechanism for Weakly-Supervised Temporal Action Localization

asrafulashiq/hamnet 3 Jan 2021

Moreover, our temporal semi-soft and hard attention modules, calculating two attention scores for each video snippet, help to focus on the less discriminative frames of an action to capture the full action boundary.

Multiple Instance Learning Weakly-supervised Temporal Action Localization +1