Attention Branch Networks (ABNs) have been shown to simultaneously provide visual explanation and improve the performance of deep convolutional neural networks (CNNs). In this work, we introduce Multi-Scale Attention Branch Networks (MSABN), which enhance the resolution of the generated attention maps, and improve the performance. We evaluate MSABN on benchmark image recognition and fine-grained recognition datasets where we observe MSABN outperforms ABN and baseline models. We also introduce a new data augmentation strategy utilizing the attention maps to incorporate human knowledge in the form of bounding box annotations of the objects of interest. We show that even with a limited number of edited samples, a significant performance gain can be achieved with this strategy.
@article{arxiv.2210.11177,
title = {Towards Better Guided Attention and Human Knowledge Insertion in Deep Convolutional Neural Networks},
author = {Ankit Gupta and Ida-Maria Sintorn},
journal= {arXiv preprint arXiv:2210.11177},
year = {2023}
}