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Feature Map Retargeting to Classify Biomedical Journal Figures.

Vinit Veerendraveer Singh, Chandra Kambhamettu

Advances in Visual Computing : ... International Symposium, ISVC ... : Proceedings. International Symposium on Visual Computing

|December 3, 2021

Summary

This summary is machine-generated.

We introduce a novel "Retarget" layer for Convolutional Neural Networks (CNNs) that improves feature map retargeting. This layer achieved state-of-the-art results in document subfigure classification without data augmentation.

Keywords:

biomedical document image classification convolutional neural networks feature maps retargeting

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Area of Science:

Computer Vision
Deep Learning
Machine Learning

Background:

Convolutional Neural Networks (CNNs) are pivotal in image analysis.
Feature map retargeting is crucial for optimizing CNN performance.
Existing methods face challenges with computational cost and memory at deeper layers.

Purpose of the Study:

To propose a novel "Retarget" layer for enhancing feature map retargeting in CNNs.
To improve the performance of CNNs in document subfigure classification tasks.
To address computational and memory inefficiencies in deep CNNs.

Main Methods:

Introduced a "Retarget" layer that densely samples feature map channels using a spatial attention regressor.
Replaced convolutional components with depthwise convolutions in a saliency-based distortion layer.
Integrated the Retarget layer into pre-trained CNNs (DenseNet121) and applied it to document subfigure classification datasets (ImageCLEF2013, 2015, 2016).
Experimented with nearest neighbor interpolation to approximate spatial sampling for efficiency.

Main Results:

The redesigned DenseNet121 with the Retarget layer achieved state-of-the-art results on the visual category of the document subfigure classification task without data augmentation.
The proposed layer demonstrated consistent improvement over baseline and other state-of-the-art attention models.
Approximation of spatial sampling showed efficiency gains while maintaining performance improvements.

Conclusions:

The novel Retarget layer effectively enhances feature map retargeting in CNNs.
The method achieves state-of-the-art performance in document subfigure classification.
The approach offers a computationally efficient solution for deep CNNs.