Attending Category Disentangled Global Context for Image Classification

TL;DR

This paper introduces a novel category disentangled global context framework that enhances image classification accuracy by guiding networks with more relevant global information, validated across multiple datasets.

Contribution

It proposes a new Category Disentangled Global Context (CDGC) model and integrates it into various architectures to improve object identification in image classification.

Findings

Improved classification accuracy across multiple datasets.

The CDGC-guided networks outperform state-of-the-art methods.

Global context encoding enhances relevance and reduces task-irrelevant information.

Abstract

In this paper, we propose a general framework for image classification using the attention mechanism and global context, which could incorporate with various network architectures to improve their performance. To investigate the capability of the global context, we compare four mathematical models and observe the global context encoded in the category disentangled conditional generative model could give more guidance as "know what is task irrelevant will also know what is relevant". Based on this observation, we define a novel Category Disentangled Global Context (CDGC) and devise a deep network to obtain it. By attending CDGC, the baseline networks could identify the objects of interest more accurately, thus improving the performance. We apply the framework to many different network architectures and compare with the state-of-the-art on four publicly available datasets. Extensive results validate the effectiveness and superiority of our approach. Code will be made public upon paper acceptance.

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