论文标题
DRT：轻巧的单图像，导致递归变压器
DRT: A Lightweight Single Image Deraining Recursive Transformer
论文作者
论文摘要
过度参数化是深度学习中的一种常见技术，可以帮助模型学习并充分概括为给定的任务；但是，这通常会导致巨大的网络结构，并在培训期间消耗大量的计算资源。有关视觉任务的最强大强大的基于变压器的深度学习模型通常具有繁重的参数和培训困难。但是，许多密集预测的低级计算机视觉任务（例如降雨条纹）通常需要在实践中具有有限的计算能力和内存的设备上执行。因此，我们介绍了一个基于本地窗口的自我发场结构，并提出了剩余连接，并提出了递归变压器（DRT）的提议，该结构具有变压器的优势，但需要少量的计算资源。特别是，通过递归体系结构，我们提出的模型仅使用当前最佳性能模型的参数数量的1.3％，同时超过了Rain100L基准上的最新方法至少0.33 dB。消融研究还研究了递归对DER结果的影响。此外，由于该模型不包含故意设计的设计，因此它也可以应用于其他图像恢复任务。我们的实验表明，它可以在逃亡方面获得竞争成果。可以在https://github.com/yc-liang/drt上找到源代码和验证模型。
Over parameterization is a common technique in deep learning to help models learn and generalize sufficiently to the given task; nonetheless, this often leads to enormous network structures and consumes considerable computing resources during training. Recent powerful transformer-based deep learning models on vision tasks usually have heavy parameters and bear training difficulty. However, many dense-prediction low-level computer vision tasks, such as rain streak removing, often need to be executed on devices with limited computing power and memory in practice. Hence, we introduce a recursive local window-based self-attention structure with residual connections and propose deraining a recursive transformer (DRT), which enjoys the superiority of the transformer but requires a small amount of computing resources. In particular, through recursive architecture, our proposed model uses only 1.3% of the number of parameters of the current best performing model in deraining while exceeding the state-of-the-art methods on the Rain100L benchmark by at least 0.33 dB. Ablation studies also investigate the impact of recursions on derain outcomes. Moreover, since the model contains no deliberate design for deraining, it can also be applied to other image restoration tasks. Our experiment shows that it can achieve competitive results on desnowing. The source code and pretrained model can be found at https://github.com/YC-Liang/DRT.
