  Deep learning models have demonstrated remarkable capabilities in learning
complex patterns and concepts from training data. However, recent findings
indicate that these models tend to rely heavily on simple and easily
discernible features present in the background of images rather than the main
concepts or objects they are intended to classify. This phenomenon poses a
challenge to image classifiers as the crucial elements of interest in images
may be overshadowed. In this paper, we propose a novel approach to address this
issue and improve the learning of main concepts by image classifiers. Our
central idea revolves around concurrently guiding the model's attention toward
the foreground during the classification task. By emphasizing the foreground,
which encapsulates the primary objects of interest, we aim to shift the focus
of the model away from the dominant influence of the background. To accomplish
this, we introduce a mechanism that encourages the model to allocate sufficient
attention to the foreground. We investigate various strategies, including
modifying the loss function or incorporating additional architectural
components, to enable the classifier to effectively capture the primary concept
within an image. Additionally, we explore the impact of different foreground
attention mechanisms on model performance and provide insights into their
effectiveness. Through extensive experimentation on benchmark datasets, we
demonstrate the efficacy of our proposed approach in improving the
classification accuracy of image classifiers. Our findings highlight the
importance of foreground attention in enhancing model understanding and
representation of the main concepts within images. The results of this study
contribute to advancing the field of image classification and provide valuable
insights for developing more robust and accurate deep-learning models.
