首页|Task-specific Part Discovery for Fine-grained Few-shot Classification

Task-specific Part Discovery for Fine-grained Few-shot Classification

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Localizing discriminative object parts(e.g.,bird head)is crucial for fine-grained classification tasks,especially for the more challenging fine-grained few-shot scenario.Previous work always relies on the learned object parts in a unified manner,where they at-tend the same object parts(even with common attention weights)for different few-shot episodic tasks.In this paper,we propose that it should adaptively capture the task-specific object parts that require attention for each few-shot task,since the parts that can distin-guish different tasks are naturally different.Specifically for a few-shot task,after obtaining part-level deep features,we learn a task-spe-cific part-based dictionary for both aligning and reweighting part features in an episode.Then,part-level categorical prototypes are gen-erated based on the part features of support data,which are later employed by calculating distances to classify query data for evaluation.To retain the discriminative ability of the part-level representations(i.e.,part features and part prototypes),we design an optimal trans-port solution that also utilizes query data in a transductive way to optimize the aforementioned distance calculation for the final predic-tions.Extensive experiments on five fine-grained benchmarks show the superiority of our method,especially for the 1-shot setting,gain-ing 0.12％,8.56％ and 5.87％ improvements over state-of-the-art methods on CUB,Stanford Dogs,and Stanford Cars,respectively.

Fine-grained image recognitionfew-shot learningtransductive learningvisual dictionarypart feature discovery

Yongxian Wei、Xiu-Shen Wei

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School of Computer Science and Technology,Nanjing University of Science and Technology,Nanjing 210094,China

National Natural Science Foundation of ChinaNatural Science Foundation of Jiangsu Province of ChinaNational Key R&D Program of ChinaFundamental Research Funds for the Central Universities,China

62272231BK202103402021YFA1001100NJ2022028

2024

10.1007/s11633-023-1451-7

机器智能研究(英文)
中国科学院自动化所

机器智能研究(英文)

CSTPCDEI
影响因子：0.49
ISSN：2731-538X
年,卷(期)：2024.21(5)