Sequential Random Network for Fine-grained Image Classification

TL;DR

This paper introduces a Sequence Random Network (SRN) that enhances deep learning models for fine-grained image classification by emphasizing detailed features, significantly improving accuracy over existing methods.

Contribution

The paper proposes a novel SRN architecture combining BiLSTM and Tanh-Dropout blocks to better capture image details in fine-grained classification tasks.

Findings

Achieved over 99% accuracy on five fine-grained datasets

Outperformed existing state-of-the-art methods

Applicable to models beyond DCNN, including Transformers

Abstract

Deep Convolutional Neural Network (DCNN) and Transformer have achieved remarkable successes in image recognition. However, their performance in fine-grained image recognition is still difficult to meet the requirements of actual needs. This paper proposes a Sequence Random Network (SRN) to enhance the performance of DCNN. The output of DCNN is one-dimensional features. This one-dimensional feature abstractly represents image information, but it does not express well the detailed information of image. To address this issue, we use the proposed SRN which composed of BiLSTM and several Tanh-Dropout blocks (called BiLSTM-TDN), to further process DCNN one-dimensional features for highlighting the detail information of image. After the feature transform by BiLSTM-TDN, the recognition performance has been greatly improved. We conducted the experiments on six fine-grained image datasets. Except for FGVC-Aircraft, the accuracies of the proposed methods on the other datasets exceeded 99%. Experimental results show that BiLSTM-TDN is far superior to the existing state-of-the-art methods. In addition to DCNN, BiLSTM-TDN can also be extended to other models, such as Transformer.