SASP: Strip-Aware Spatial Perception for Fine-Grained Bird Image Classification
Zheng Wang

TL;DR
This paper introduces a strip-aware spatial perception framework with novel modules to improve fine-grained bird image classification by capturing long-range spatial dependencies, resulting in enhanced robustness and interpretability.
Contribution
It proposes a new framework with EPA and CSW modules that effectively model long-range spatial dependencies in bird images, improving classification accuracy.
Findings
Achieves significant performance improvements on CUB-200-2011 dataset.
Enhances model robustness and interpretability in fine-grained bird classification.
Maintains architectural efficiency with the proposed modules.
Abstract
Fine-grained bird image classification (FBIC) is not only of great significance for ecological monitoring and species identification, but also holds broad research value in the fields of image recognition and fine-grained visual modeling. Compared with general image classification tasks, FBIC poses more formidable challenges: 1) the differences in species size and imaging distance result in the varying sizes of birds presented in the images; 2) complex natural habitats often introduce strong background interference; 3) and highly flexible poses such as flying, perching, or foraging result in substantial intra-class variability. These factors collectively make it difficult for traditional methods to stably extract discriminative features, thereby limiting the generalizability and interpretability of models in real-world applications. To address these challenges, this paper proposes a…
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