Weakly supervised marine animal detection from remote sensing images using vector-quantized variational autoencoder
This work addresses monitoring marine ecosystems to mitigate human impacts, but it is incremental as it adapts an existing method to a new domain.
The paper tackles weakly-supervised marine animal detection from aerial images by adapting a vector-quantized variational autoencoder for anomaly detection, achieving superior performance over existing methods on two datasets.
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.