  This paper studies a reconstruction-based approach for weakly-supervised
animal detection from aerial images in marine environments. Such an approach
leverages an anomaly detection framework that computes metrics directly on the
input space, enhancing interpretability and anomaly localization compared to
feature embedding methods. Building upon the success of Vector-Quantized
Variational Autoencoders in anomaly detection on computer vision datasets, we
adapt them to the marine animal detection domain and address the challenge of
handling noisy data. To evaluate our approach, we compare it with existing
methods in the context of marine animal detection from aerial image data.
Experiments conducted on two dedicated datasets demonstrate the superior
performance of the proposed method over recent studies in the literature. Our
framework offers improved interpretability and localization of anomalies,
providing valuable insights for monitoring marine ecosystems and mitigating the
impact of human activities on marine animals.
