Dual Transformer With Multi-Grained Assembly for Fine-Grained Visual Classification

Fine-grained visual classification requires distinguishing sub-categories within the same super-category, which suffers from small inter-class and large intra-class variances. This paper aims to improve the FGVC task towards better performance, for which we deliver a novel dual Transformer framework (coined Dual-TR) with multi-grained assembly. The Dual-TR is well-designed to encode fine-grained objects by two parallel hierarchies, which is amenable to capturing the subtle yet discriminative cues via the self-attention mechanism in ViT. Specifically, we perform orthogonal multi-grained assembly within the Transformer structure for a more robust representation, i.e., intra-layer and inter-layer assembly. The former aims to explore the informative feature in various self-attention heads within the Transformer layer. The latter pays attention to the token assembly across Transformer layers. Meanwhile, we introduce the constraint of center loss to pull intra-class samples' compactness and push that of inter-class samples. Extensive experiments show that Dual-TR performs on par with the state-of-the-art methods on four public benchmarks, including CUB-200-2011, NABirds, iNaturalist2017, and Stanford Dogs. The comprehensive ablation studies further demonstrate the effectiveness of architectural design choices.