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：深度学习模型在学习训练数据中的复杂模式和概念方面展现了卓越能力。然而，近期研究表明，这些模型往往过度依赖图像背景中简单且易于区分的特征，而非其目标分类的主要概念或对象。这一现象对图像分类器构成了挑战，因为图像中关键感兴趣元素可能被遮蔽。本文提出了一种新颖方法以解决该问题，并改进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步引导模型关注前景。通过强调包含主要目标物体的前景，旨在将模型焦点从背景的显著影响中转移开来。为此，我们引入了一种机制，鼓励模型将充分注意力分配至前景。我们研究了多种策略，包括修改损失函数或增加额外架构组件，以使分类器能够有效捕捉图像中的主要概念。此外，我们探索了不同前景注意力机制对模型性能的影响，并深入分析了其有效性。通过在基准数据集上的广泛实验，我们证明了所提方法在提升图像分类器分类精度方面的有效性。研究结果凸显了前景注意力在增强模型理解与表征图像核心概念中的关键作用。本研究的成果有助于推动图像分类领域的发展，并为开发更稳健、精准的深度学习模型提供了宝贵见解。