MobileNet-SA: Lightweight CNN with Self Attention for Sketch Classification

Sketch classification plays a crucial role across diverse domains, including image retrieval, artistic style analysis, and content-based image retrieval. While CNNs have demonstrated remarkable success in various image-related tasks, the computational complexity of large models poses challenges in resource-constrained environments. To address this concern, we propose MobileNet-SA, a novel lightweight model that seamlessly integrates a self-attention module into the MobileNet architecture, with a specific focus on enhancing sketch classification performance. The MobileNet-SA model leverages the inherent efficiency of lightweight CNN while harnessing the power of self-attention mechanisms to effectively capture spatial dependencies and enrich feature representations within sketch data. In our experiments, MobileNet-SA achieves state-of-the-art results, demonstrating an impressive accuracy of 93.5% on the challenging SketchyCOCO dataset and 96.7% on the GM-Sketch dataset. We thoroughly evaluate the model’s performance across diverse sketch classes, confirming its robustness and generalization capabilities, which make it well-suited for real-world applications where input sketches may exhibit significant variations. Our research indicates that MobileNet-SA not only outperforms existing methods but also offers an efficient and interpretable solution for sketch classification tasks.