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Image Captioning Using Motion-CNN with Object Detection.

Kiyohiko Iwamura¹, Jun Younes Louhi Kasahara¹, Alessandro Moro^1,2

¹Department of Precision Engineering, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo 113-8656, Japan.

Sensors (Basel, Switzerland)

|February 13, 2021

Summary

This summary is machine-generated.

This study enhances automatic image captioning by refining the use of motion features. The new method improves accuracy by selectively using relevant motion data, benefiting visually impaired users and image indexing.

Keywords:

deep learning image captioning motion estimation object detection

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Area of Science:

Computer Vision
Artificial Intelligence
Natural Language Processing

Background:

Deep learning models excel at image captioning by linking image features with words.
Existing methods struggle with verb generation as image features alone are insufficient.
Prior work incorporated motion features but used all, leading to accuracy decline.

Purpose of the Study:

To analyze the impact of motion features on image captioning accuracy.
To propose a novel, end-to-end trainable method for improved image caption generation.
To address the accuracy decrease caused by irrelevant motion features.

Main Methods:

Experimental analysis of motion feature contribution to captioning.
Development of a selective motion feature integration technique.
End-to-end training of the proposed image caption generation model.

Main Results:

Identified that not all motion features positively contribute to captioning.
Demonstrated that unnecessary motion features decrease captioning accuracy.
Achieved improved caption generation performance on MSR-VTT2016-Image, MSCOCO, and custom datasets.

Conclusions:

Selective use of motion features is crucial for accurate image captioning.
The proposed method effectively alleviates accuracy reduction from irrelevant motion data.
This approach advances automatic image captioning for applications like visual assistance and image indexing.