Concept Drift and Long-Tailed Distribution in Fine-Grained Visual Categorization: Benchmark and Method
Ye, Shuo Chen, Shiming Wang, Ruxin Wu, Tianxu Khan, Salman Khan, Fahad Shahbaz Shao, Ling
Ye, Shuo
Chen, Shiming
Wang, Ruxin
Wu, Tianxu
Khan, Salman
Khan, Fahad Shahbaz
Shao, Ling
Author
Ye, Shuo
Chen, Shiming
Wang, Ruxin
Wu, Tianxu
Khan, Salman
Khan, Fahad Shahbaz
Shao, Ling
Chen, Shiming
Wang, Ruxin
Wu, Tianxu
Khan, Salman
Khan, Fahad Shahbaz
Shao, Ling
Supervisor
Department
Computer Vision
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Journal article
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English
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Abstract
Data is the foundation for the development of computer vision, and the establishment of datasets plays an important role in advancing the techniques of fine-grained visual categorization (FGVC). In the existing FGVC datasets used in computer vision, it is generally assumed that each collected instance has fixed characteristics and the distribution of different categories is relatively balanced. In contrast, the real world scenario reveals the fact that the characteristics of instances tend to vary with time and exhibit a long-tailed distribution. Hence, the collected datasets may mislead the optimization of the fine-grained classifiers, resulting in unpleasant performance in real applications. Starting from the real-world conditions and to promote the practical progress of fine-grained visual categorization, we present a Concept Drift and Long-Tailed Distribution (CDLT) dataset. Specifically, the dataset is collected by gathering 11195 images of 250 instances in different species for 47 consecutive months in their natural contexts. The collection process involves dozens of crowd workers for photographing and domain experts for labeling. Meanwhile, we propose a feature recombination framework to address the learning challenges associated with CDLT. Experimental results validate the efficacy of our method while also highlighting the limitations of popular large vision-language models (e.g., CLIP) in the context of long-tailed distributions. This emphasizes the significance of CDLT as a benchmark for investigating these challenges. The dataset is available at: https://github.com/SYe-hub/CDLT.
Citation
S. Ye, S. Chen, R. Wang, T. Wu, S. Khan, F.S. Khan , et al., "Concept Drift and Long-Tailed Distribution in Fine-Grained Visual Categorization: Benchmark and Method," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. PP, no. 99, pp. 1-17, 2026, https://doi.org/10.1109/tpami.2026.3674763.
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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Keywords
46 Information and Computing Sciences, 4605 Data Management and Data Science, 4608 Human-Centred Computing, 4611 Machine Learning
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IEEE