Background Invariant Classification on Infrared Imagery by Data Efficient Training and Reducing Bias in CNNs

TL;DR

This paper introduces a split training method for CNNs that reduces background bias in infrared and RGB image classification, leading to more accurate and human-like focus on object shape and structure.

Contribution

A novel two-step split training procedure that minimizes background bias in CNNs, improving classification accuracy on infrared and RGB images.

Findings

Outperforms traditional training in simple and deep CNNs

Learns to focus on shape and structure rather than background

Enhances robustness to background variations

Abstract

Even though convolutional neural networks can classify objects in images very accurately, it is well known that the attention of the network may not always be on the semantically important regions of the scene. It has been observed that networks often learn background textures which are not relevant to the object of interest. In turn this makes the networks susceptible to variations and changes in the background which negatively affect their performance. We propose a new two-step training procedure called split training to reduce this bias in CNNs on both Infrared imagery and RGB data. Our split training procedure has two steps: using MSE loss first train the layers of the network on images with background to match the activations of the same network when it is trained using images without background; then with these layers frozen, train the rest of the network with cross-entropy loss to classify the objects. Our training method outperforms the traditional training procedure in both a simple CNN architecture, and deep CNNs like VGG and Densenet which use lots of hardware resources, and learns to mimic human vision which focuses more on shape and structure than background with higher accuracy.