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GKA: Graph-guided knowledge association for fine-grained visual categorization
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DOI：10.1016/j.neucom.2025.129819.png)
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Abstract
En 中文
Fine-grained visual categorization aims to distinguish highly similar subclasses by exploiting subtle differences. However, existing methods predominantly emphasize the extraction of visual cues from individual images, overlooking the exploration of semantic relationships within and across classes. To this end, we introduce a novel approach termed Graph-based Knowledge Association (GKA). Specifically, we employ a positional embedding to model the relationship between instances in the feature space, and adaptively mine the connections between features of different instances via a graph neural network. The framework effectively aggregates features from neighboring nodes to enhance the understanding of discriminative features by exploiting complementary information between instances. Furthermore, a plain knowledge-guided module embeds this relational knowledge into the training of the backbone network for discriminative feature extraction, thus improving fine-grained classification performance. Empirical evaluations on four benchmark datasets for Fine-grained Visual Categorization (FGVC) demonstrate that our method achieves state-of-the-art (SOTA) performance.
Keywords:
Computer vision
Deep learning
Fine-grained visual categorization
Graph neural networks