Equivalent Classification Mapping for Weakly Supervised Temporal Action Localization

08/18/2020
by   Le Yang, et al.
0

Weakly supervised temporal action localization is a newly emerging yet widely studied topic in recent years. The existing methods can be categorized into two localization-by-classification pipelines, i.e., the pre-classification pipeline and the post-classification pipeline. The pre-classification pipeline first performs classification on each video snippet and then aggregate the snippet-level classification scores to obtain the video-level classification score, while the post-classification pipeline aggregates the snippet-level features first and then predicts the video-level classification score based on the aggregated feature. Although the classifiers in these two pipelines are used in different ways, the role they play is exactly the same—to classify the given features to identify the corresponding action categories. To this end, an ideal classifier can make both pipelines work. This inspires us to simultaneously learn these two pipelines in a unified framework to obtain an effective classifier. Specifically, in the proposed learning framework, we implement two parallel network streams to model the two localization-by-classification pipelines simultaneously and make the two network streams share the same classifier, thus achieving the novel Equivalent Classification Mapping (ECM) mechanism. Considering that an ideal classifier would make the classification results of the two network streams be identical and make the frame-level classification scores obtained from the pre-classification pipeline and the feature aggregation weights in the post-classification pipeline be consistent, we further introduce an equivalent classification loss and an equivalent weight transition module to endow the proposed learning framework with such properties. Comprehensive experiments are carried on three benchmarks and the proposed ECM achieves superior performance over other state-of-the-art methods.

READ FULL TEXT

page 9

page 19

research
12/29/2021

Background-aware Classification Activation Map for Weakly Supervised Object Localization

Weakly supervised object localization (WSOL) relaxes the requirement of ...
research
03/30/2021

Weakly Supervised Temporal Action Localization Through Learning Explicit Subspaces for Action and Context

Weakly-supervised Temporal Action Localization (WS-TAL) methods learn to...
research
08/07/2019

Adversarial Seeded Sequence Growing for Weakly-Supervised Temporal Action Localization

Temporal action localization is an important yet challenging research to...
research
08/12/2021

Deep Motion Prior for Weakly-Supervised Temporal Action Localization

Weakly-Supervised Temporal Action Localization (WSTAL) aims to localize ...
research
08/24/2023

Cross-Video Contextual Knowledge Exploration and Exploitation for Ambiguity Reduction in Weakly Supervised Temporal Action Localization

Weakly supervised temporal action localization (WSTAL) aims to localize ...
research
10/24/2019

LPAT: Learning to Predict Adaptive Threshold for Weakly-supervised Temporal Action Localization

Recently, Weakly-supervised Temporal Action Localization (WTAL) has been...
research
05/17/2018

NeuralNetwork-Viterbi: A Framework for Weakly Supervised Video Learning

Video learning is an important task in computer vision and has experienc...

Please sign up or login with your details

Forgot password? Click here to reset