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Found 5 papers, 1 papers with code
Adaptive Uncertainty Distribution in Deep Learning for Unsupervised Underwater Image Enhancement
This makes it difficult to train supervised deep learning models on large and diverse datasets, which can limit the model's performance.
A lightweight Transformer-based model for fish landmark detection
Transformer-based models, such as the Vision Transformer (ViT), can outperform onvolutional Neural Networks (CNNs) in some vision tasks when there is sufficient training data.
Unsupervised Fish Trajectory Tracking and Segmentation
In the third stage, the refined labels are used to train a segmentation network.
Applications of Deep Learning in Fish Habitat Monitoring: A Tutorial and Survey
This paper is written to serve as a tutorial for marine scientists who would like to grasp a high-level understanding of DL, develop it for their applications by following our step-by-step tutorial, and see how it is evolving to facilitate their research efforts.
Transformer-based Self-Supervised Fish Segmentation in Underwater Videos
Our proposed model is trained on videos -- without any annotations -- to perform fish segmentation in underwater videos taken in situ in the wild.
