The classification of bird species through visual data plays a vital role in biodiversity monitoring, conservation efforts, and ecological research. Traditional deep learning approaches have achieved significant progress in image classification tasks but remain heavily reliant on large-scale annotated datasets. This poses a substantial challenge in ecological domains where labelled data is scarce, particularly for rare or geographically
constrained species. In contrast, Few-Shot Learning (FSL) paradigms enable models to generalize to new classes with only a handful of labelled examples, offering a data-efficient alternative that more closely mimics human learning capabilities. A fundamental challenge in species classification lies in the visual heterogeneity and inter-class similarity of bird imagery. Species may differ subtly in appearance, and visual conditions (e.g., lighting, pose, background clutter) add further variability. This complexity limits the effectiveness of conventional models, particularly when the dataset is small or unbalanced. Few-shot learning addresses this issue by adopting paradigms such as metric-based learning and episodic training, which aim to develop generalizable embeddings that can distinguish between classes even with limited supervision. This research seeks to evaluate the effectiveness of multiple FSL paradigms across such constrained settings, using a curated dataset of 20 bird species containing limited training examples per class.
The goal of this thesis is to conduct a comprehensive evaluation of FSL methods for fine-grained bird species classification under low-data regimes. Specifically, the work aims to assess the performance of representative FSL paradigms—including metric-based approaches like Prototypical Networks, Matching Networks, and Relation Networks – using lightweight pretrained convolutional encoders. The methodology will involve simulating N-way K-shot classification tasks, where a small number of labelled support examples are provided for a subset of classes, and performance is measured on query samples from the same set. Evaluation will be conducted using standardized metrics such as classification accuracy, confusion matrices, precision-recall and F1 scores, as well as t-SNE-based visualization of the learned feature space. This framework will allow for a structured comparison of how well each paradigm generalizes to novel classification tasks.
The anticipated outcome of this thesis is the identification of the most suitable FSL approach for bird species classification with limited data, alongside the development of a reusable pipeline that can be extended to similar ecological image recognition tasks. The expected contributions include:
– A comparative analysis of few-shot learning paradigms for bird image classification
– Quantitative insights into model generalization and accuracy under N-way K-shot conditions
– A reproducible framework for evaluating FSL models in ecological classification scenarios
By identifying the strengths and limitations of each FSL approach in relation to fine-grained, data-scarce classification problems, this research aims to support the deployment of intelligent, lightweight recognition systems for wildlife conservation and ecological informatics.