Underwater fish recognition is an important task in fish stock assessment and marine ecosystem studies. Machine learning techniques have been applied to train high-performance fish recognition models from underwater images. However, underwater images often contain extremely noisy backgrounds, hindering the training of accurate recognition models. Traditional methods exploit handcrafted features to train traditional classifiers. These methods often suffer from low recognition accuracy and limited scalability to large-scale datasets. While deep learning approaches have been proposed, the challenge of learning with noisy underwater images has not yet been fully addressed. We propose a discriminative feature learning (DFL) framework to train accurate fish recognition models on noisy underwater images. By leveraging the idea of contrastive learning, DFL encourages the model to learn more discriminative features for images in different classes and similar features for images in the same class. To better address the noisy background problem, DFL also utilizes a regularization technique called attention suppression to prevent the model from paying too much attention to the noisy background. Experimental results on three benchmark datasets validate the superior performance of DFL over the current state-of-the-art deep learning approaches.

中文翻译：

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判别特征学习用于水下鱼类识别

水下鱼类识别是鱼类种群评估和海洋生态系统研究中的重要任务。机器学习技术已应用于从水下图像训练高性能鱼识别模型。但是，水下图像通常包含非常嘈杂的背景，从而阻碍了对准确识别模型的训练。传统方法利用手工制作的特征来训练传统分类器。这些方法经常遭受识别精度低和对大规模数据集的可扩展性有限的问题。虽然已经提出了深度学习方法，但是尚未充分解决利用嘈杂的水下图像进行学习的挑战。我们提出了一种判别性特征学习（DFL）框架，以在嘈杂的水下图像上训练精确的鱼类识别模型。通过利用对比学习的思想，DFL鼓励模型为不同类别的图像学习更多区分特征，并为同一类别的图像学习更多相似特征。为了更好地解决嘈杂的背景问题，DFL还使用一种称为注意力抑制的正则化技术来防止模型过多关注嘈杂的背景。在三个基准数据集上的实验结果验证了DFL在当前最先进的深度学习方法上的优越性能。