论文标题
克服水下鱼片分割中的注释瓶颈：一种强大的自我监督学习方法
Overcoming Annotation Bottlenecks in Underwater Fish Segmentation: A Robust Self-Supervised Learning Approach
论文作者
论文摘要
由于可见性，可变照明和动态背景，在水下视频中的准确鱼片分割具有挑战性，因此对许多应用进行手动注释不切实际，制造了完全监督的方法。本文介绍了一种新型的自我监督学习方法，用于使用深度学习进行鱼类分割。我们的模型未经手动注释训练，通过在增强视图中对齐功能并实施时空的一致性来学习强大而可推广的表示。我们证明了它在三个具有挑战性的水下视频数据集上的有效性：深鱼，海草和YouTube-Vos，超过了现有的自我监督方法，并实现了与完全监督的方法相当的细分精度而无需进行昂贵的注释。经过深深的培训，我们的模型表现出强烈的概括，在看不见的海草和YouTube-VOS数据集上实现了高分段的准确性。此外，由于其并行处理和有效的锚定采样技术，我们的模型在计算上是有效的，因此它适用于实时应用程序和边缘设备上的潜在部署。我们使用jaccard索引和骰子系数以及定性比较提出了定量结果，展示了我们进步水下视频分析的准确性，鲁棒性和效率
Accurate fish segmentation in underwater videos is challenging due to low visibility, variable lighting, and dynamic backgrounds, making fully-supervised methods that require manual annotation impractical for many applications. This paper introduces a novel self-supervised learning approach for fish segmentation using Deep Learning. Our model, trained without manual annotation, learns robust and generalizable representations by aligning features across augmented views and enforcing spatial-temporal consistency. We demonstrate its effectiveness on three challenging underwater video datasets: DeepFish, Seagrass, and YouTube-VOS, surpassing existing self-supervised methods and achieving segmentation accuracy comparable to fully-supervised methods without the need for costly annotations. Trained on DeepFish, our model exhibits strong generalization, achieving high segmentation accuracy on the unseen Seagrass and YouTube-VOS datasets. Furthermore, our model is computationally efficient due to its parallel processing and efficient anchor sampling technique, making it suitable for real-time applications and potential deployment on edge devices. We present quantitative results using Jaccard Index and Dice coefficient, as well as qualitative comparisons, showcasing the accuracy, robustness, and efficiency of our approach for advancing underwater video analysis
