Semantic Topological Multi-Scale Part Network for Fine-Grained Visual Classification

Abstract

Fine-grained visual classification (FGVC) aims to distinguish highly similar subcategories, and its performance relies heavily on the accurate modeling of discriminative local parts and their structural relationships. However, existing Vision Transformer-based methods are susceptible to background noise interference, and the relationship modeling approach relying on explicit spatial coordinates struggles to maintain stable structural representations when targets undergo pose variations and non-rigid deformations. To address these issues, this paper proposes a Semantic Topology Part Network (STP-Net). First, a Prior-Guided Part Aggregator (PGA) is designed, which leverages the foreground prior provided by foundation models to guide discriminative part discovery, enhancing target region responses while suppressing background interference. Second, a Topology-Informed Semantic Graph Convolutional Network (TIS-GCN) is designed to dynamically construct topological relationships among parts in an implicit semantic space, achieving robust modeling against complex structural variations. Furthermore, a Semantic–Spatial Cross-Attention (SSCA) mechanism is introduced to establish bidirectional interaction between semantic relationships and spatial features, and combined with a Global-Context Adaptive Gating mechanism to accomplish multi-scale feature fusion. On four mainstream fine-grained visual classification benchmarks, namely CUB-200-2011, Stanford Cars, Stanford Dogs, and NABirds, the proposed model achieves Top-1 accuracies of 92.7%, 94.9%, 95.2%, and 92.3%, respectively. Comprehensive ablation studies and visualization analyses further validate the effectiveness of the proposed method in background suppression, structural relationship modeling, and discriminative feature learning.