EnGraf-Net: Multiple Granularity Branch Network with Fine-Coarse Graft Grained for Classification Task
This addresses the problem of distinguishing highly similar objects in fine-grained classification for computer vision applications, offering an incremental improvement by using hierarchical semantic associations instead of part annotations.
The paper tackled fine-grained classification by leveraging semantic associations from a hierarchy as supervised signals in an end-to-end deep neural network, achieving competitive performance with state-of-the-art approaches on datasets like CIFAR-100, CUB-200-2011, and FGVC-Aircraft without needing cropping or manual annotations.
Fine-grained classification models are designed to focus on the relevant details necessary to distinguish highly similar classes, particularly when intra-class variance is high and inter-class variance is low. Most existing models rely on part annotations such as bounding boxes, part locations, or textual attributes to enhance classification performance, while others employ sophisticated techniques to automatically extract attention maps. We posit that part-based approaches, including automatic cropping methods, suffer from an incomplete representation of local features, which are fundamental for distinguishing similar objects. While fine-grained classification aims to recognize the leaves of a hierarchical structure, humans recognize objects by also forming semantic associations. In this paper, we leverage semantic associations structured as a hierarchy (taxonomy) as supervised signals within an end-to-end deep neural network model, termed EnGraf-Net. Extensive experiments on three well-known datasets CIFAR-100, CUB-200-2011, and FGVC-Aircraft demonstrate the superiority of EnGraf-Net over many existing fine-grained models, showing competitive performance with the most recent state-of-the-art approaches, without requiring cropping techniques or manual annotations.