Image Labels Are All You Need for Coarse Seagrass Segmentation

TL;DR

This paper introduces a weakly supervised approach for coarse seagrass segmentation using only image-level labels, leveraging large language models and contrastive learning to outperform previous patch-label methods.

Contribution

It proposes SeaFeats and SeaCLIP architectures that utilize unsupervised pre-training and language models, enabling effective seagrass classification with fewer labels and improved accuracy.

Findings

Outperforms previous patch-label methods by 6.8% in F1 score.

Achieves 12.1% higher F1 score on seagrass presence/absence detection.

Demonstrates robustness through real-world case studies.

Abstract

Seagrass meadows serve as critical carbon sinks, but estimating the amount of carbon they store requires knowledge of the seagrass species present. Underwater and surface vehicles equipped with machine learning algorithms can help to accurately estimate the composition and extent of seagrass meadows at scale. However, previous approaches for seagrass detection and classification have required supervision from patch-level labels. In this paper, we reframe seagrass classification as a weakly supervised coarse segmentation problem where image-level labels are used during training (25 times fewer labels compared to patch-level labeling) and patch-level outputs are obtained at inference time. To this end, we introduce SeaFeats, an architecture that uses unsupervised contrastive pre-training and feature similarity, and SeaCLIP, a model that showcases the effectiveness of large language models as a supervisory signal in domain-specific applications. We demonstrate that an ensemble of SeaFeats and SeaCLIP leads to highly robust performance. Our method outperforms previous approaches that require patch-level labels on the multi-species 'DeepSeagrass' dataset by 6.8% (absolute) for the class-weighted F1 score, and by 12.1% (absolute) for the seagrass presence/absence F1 score on the 'Global Wetlands' dataset. We also present two case studies for real-world deployment: outlier detection on the Global Wetlands dataset, and application of our method on imagery collected by the FloatyBoat autonomous surface vehicle.