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.

翻译：深度学习模型在从训练数据中学习复杂模式和概念方面展现出卓越的能力。然而，近期研究指出，这些模型往往过度依赖图像背景中简单且易于捕捉的特征，而非其意图分类的主要概念或对象。这一现象对图像分类器构成挑战，因为图像中关键要素的重要性可能被削弱。本文提出一种新方法以解决此问题，并改善图像分类器对主要概念的学习。我们的核心思路是在分类任务中同时引导模型关注前景区域。通过强调包含主要目标对象的前景，旨在将模型注意力从背景的主导影响中转移开。为实现这一目标，我们引入一种机制，鼓励模型将充分注意力分配给前景。我们探究了多种策略，包括修改损失函数或添加额外架构组件，以使分类器有效捕捉图像中的主要概念。此外，我们分析了不同前景注意力机制对模型性能的影响，并阐释其有效性。通过在基准数据集上的大量实验，我们证明了所提方法在提升图像分类器分类准确率方面的有效性。研究结果凸显了前景注意力在增强模型对图像主要概念理解与表征中的重要性。本研究的成果有助于推进图像分类领域的发展，并为开发更稳健、更精准的深度学习模型提供重要启示。