A Bi-Prototype BDC Metric Network With Lightweight Adaptive Task Attention for Few-Shot Fine-Grained Ship Classification in Remote Sensing Images

This is the authors' abstract. We don't add key points for this paper.

Fine-grained ship classification in optical remote sensing images is a major challenge in the ocean observation field, elaborated as follows: First, the cost of acquiring ship images is expensive. Obtaining numerous labeled samples is difficult, resulting in the poor generalization ability of training models. Second, the features of ship target cannot be accurately obtained owing to complex background interference. Third, inter-class similarity and intra-class diversity among different ships render ship classification difficult. In this study, we propose LATA-BP-BDC: a bi-prototype Brownian Distance Covariance (BDC) metric network with lightweight adaptive task attention (LATA) for few-shot fine-grained ship classification. First, the LATA module is used to generate 3-dimensional (3D) weights, which can effectively reduce complex background interference and improve the adaptive capturing ability of target features without including additional network operators. Second, we input target features into the BDC metric module and output the BDC matrices to represent image information. Because the similarity between two images can be calculated as the corresponding BDC matrices distance, the improvement of the relevance of similar targets can be realized. Finally, we use the bi-prototype module to generate highly accurate prototypes, further calibrating information differences between images, which enhances the correlation between the same category samples and separability between different categories samples. Consequently, this process effectively reduces the influence of large intra-class appearance variation and small inter-class appearance variation. We perform validation using two fine-grained datasets, FGSCR and CUB. Compared with state-of-the-art methods, the LATA-BP-BDC achieves a superior performance and has good generalization for fine-grained few-shot classification.