海底画像に対する位置情報正則化自己教師あり特徴学習の検討
Investigating Location-Regularised Self-Supervised Feature Learning for Seafloor Visual Imagery
海底のロボット撮影画像を対象に、位置メタデータを用いた正則化が6種類の自己教師あり学習フレームワークの下流分類性能を一貫して向上させることを示した研究。
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著者: Cailei Liang, Adrian Bodenmann, Emma J Curtis, Samuel Simmons, Kazunori Nagano, Stan Brown, Adam Riese, Blair Thornton
分類: cs.CV, cs.RO
原文アブストラクト
High-throughput interpretation of robotically gathered seafloor visual imagery can increase the efficiency of marine monitoring and exploration. Although recent research has suggested that location metadata can enhance self-supervised feature learning (SSL), its benefits across different SSL strategies, models and seafloor image datasets are underexplored. This study evaluates the impact of location-based regularisation on six state-of-the-art SSL frameworks, which include Convolutional Neural Network (CNN) and Vision Transformer (ViT) models with varying latent-space dimensionality. Evaluation across three diverse seafloor image datasets finds that location-regularisation consistently improves downstream classification performance over standard SSL, with average F1-score gains of $4.9 \pm 4.0%$ for CNNs and $6.3 \pm 8.9%$ for ViTs, respectively. While CNNs pretrained on generic datasets benefit from high-dimensional latent representations, dataset-optimised SSL achieves similar performance across the high (512) and low (128) dimensional latent representations. Location-regularised SSL improves CNN performance over pre-trained models by $2.7 \pm 2.7%$ and $10.1 \pm 9.4%$ for high and low-dimensional latent representations, respectively. For ViTs, high-dimensionality benefits both pre-trained and dataset-optimised SSL. Although location-regularisation improves SSL performance compared to standard SSL methods, pre-trained ViTs show strong generalisation, matching the best-performing location-regularised SSL with F1-scores of $0.795 \pm 0.075$ and $0.795 \pm 0.077$, respectively. The findings highlight the value of location metadata for SSL regularisation, particularly when using low-dimensional latent representations, and demonstrate strong generalisation of high-dimensional ViTs for seafloor image analysis.
