Attention-X: Enhancing the classification of natural attraction scenes with advanced attention mechanisms - Mungklachaiya, Sujitranan; Salaiwarakul, Anongporn (Engineering and Applied Science Research, 2025)
This paper proposes the Attention-X method, which is an attention-based framework designed to address the challenges of interclass similarity and intraclass variance in natural scene classification tasks. The proposed method enhances pretrained convolutional neural networks (CNNs) by integrating an attention mechanism that selectively emphasizes salient and discriminative features, which enables the model to more effectively differentiate between visually similar scenes and manage variations within the same class. The proposed Attention-X method generates attention maps aligned with extracted features, integrating spatial representations with channel-wise relevance to overcome the limitations of the original deep features. This fusion enables the model to selectively amplify meaningful feature activations while suppressing irrelevant or redundant information. This improves the model’s ability to distinguish between visually similar scenes and to handle variations within the same class. The proposed method was evaluated on the widely used SUN397, ADE20K, and Places365 benchmark datasets. The experimental results demonstrate that the proposed Attention-X method improves classification accuracy while maintaining competitive model complexity, outperforming several state-of-the-art methods. These findings highlight the effectiveness of the proposed method in real-world scenarios where subtle interclass differences and intraclass variability pose significant challenges.