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Question-Guided Erasing-Based Spatiotemporal Attention Learning for Video Question Answering.

Fei Liu, Jing Liu, Richang Hong

IEEE Transactions on Neural Networks and Learning Systems

|August 31, 2021

Summary

This summary is machine-generated.

This study introduces a novel method for video question answering (VideoQA) by learning discriminative spatiotemporal attention. The approach enforces correlations between attended and non-attended video regions, improving answer accuracy.

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Area of Science:

Computer Science
Artificial Intelligence
Machine Learning

Background:

Video question answering (VideoQA) is challenged by isolated treatment of attended and non-attended regions.
Existing spatiotemporal attention methods lack discriminative power.

Purpose of the Study:

To develop a discriminative spatiotemporal attention learning method for VideoQA.
To enforce correlations between attention and non-attention features using a distance constraint.

Main Methods:

Introduced an attention-guided erasing mechanism to generate attention and non-attention features.
Applied a metric learning loss to enforce distance between feature types, enhancing attention discriminability.
Developed a pyramid variant to incorporate multiscale spatiotemporal information.

Main Results:

Achieved state-of-the-art performance on multiple VideoQA datasets.
Demonstrated improved discriminative spatiotemporal attention distribution.
Validated effectiveness through comprehensive ablation studies.

Conclusions:

The proposed method enhances VideoQA accuracy by learning discriminative spatiotemporal attention.
Enforcing feature correlation improves model's ability to focus on relevant video segments.
The approach offers a computationally efficient way to boost VideoQA performance.