虽然在开发模型上做了很多工作来解决视觉问题的问题的问题，但这些模型将问题与图像功能相关的能力仍然不那么探索。我们介绍了不同损耗功能的不同特征提取方法的实证研究。我们为视觉问题的任务提出了新的数据集，其中多个图像输入只有一个地面真理，并在它们上基准测试我们的结果。我们的最终模型利用Reset + RCNN图像特征和BERT Embedings，灵感来自堆叠注意力网络，在Clever + Tinyimagenet数据集中提供39％的字精度和99％的图像精度。

We propose an efficient and generative augmentation approach to solve the inadequacy concern of underwater debris data for visual detection. We use cycleGAN as a data augmentation technique to convert openly available, abundant data of terrestrial plastic to underwater-style images. Prior works just focus on augmenting or enhancing existing data, which moreover adds bias to the dataset. Compared to our technique, which devises variation, transforming additional in-air plastic data to the marine background. We also propose a novel architecture for underwater debris detection using an attention mechanism. Our method helps to focus only on relevant instances of the image, thereby enhancing the detector performance, which is highly obliged while detecting the marine debris using Autonomous Underwater Vehicle (AUV). We perform extensive experiments for marine debris detection using our approach. Quantitative and qualitative results demonstrate the potential of our framework that significantly outperforms the state-of-the-art methods.