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Found 7 papers, 2 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.
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.
Computer Vision and Deep Learning for Fish Classification in Underwater Habitats: A Survey
This information is essential for developing sustainable fisheries for human consumption, and for preserving the environment.
A Realistic Fish-Habitat Dataset to Evaluate Algorithms for Underwater Visual Analysis
The dataset consists of approximately 40 thousand images collected underwater from 20 \green{habitats in the} marine-environments of tropical Australia.
Underwater Fish Detection with Weak Multi-Domain Supervision
Given a sufficiently large training dataset, it is relatively easy to train a modern convolution neural network (CNN) as a required image classifier.
