Efficient Fine-grained Bird Classification Via Dynamic Ensembles and Active Learning

Abstract

Fine-grained bird species classification is a challenging task, mainly because the differences between species are often very subtle, and there is typically limited labeled data available for each species. This paper introduces a novel approach that combines multiple deep-learning models with a smart labeling strategy to address these issues. The method uses a dynamic ensemble of three advanced models: ResNet50, which excels in recognizing visual patterns; EfficientNet-B3, known for its efficiency and performance balance; and Swin Transformer, a newer model that captures both local and global features in images. Instead of relying on each model’s prediction separately, an adaptive mechanism (a multi-layer perceptron or MLP) weighs their predictions per sample, allowing the system to make smarter and more accurate decisions. Overall, the approach stands out due to its combination of a dynamic ensemble of models and an active learning strategy. This not only boosts performance but also makes the best use of limited labeled data, offering a more efficient way to tackle complex classification tasks like bird species identification. The results demonstrate that with fewer labels, the method can still achieve high accuracy and generalize well, providing a powerful tool for fine-grained classification in scenarios where data is scarce.