标题： IO Transformer：评估基于SwinV2的奖励模型在计算机视觉中的应用 显示英文标题

标题： IO Transformer: Evaluating SwinV2-Based Reward Models for Computer Vision

摘要： 变压器及其衍生模型在文本、视觉和语音识别任务中取得了最先进的性能。 然而，很少有努力致力于训练能够评估其他模型输出质量的变压器。 本文研究了基于SwinV2的奖励模型，称为输入输出变压器（IO变压器）和输出变压器。 这些奖励模型可用于推理质量评估、数据分类和策略优化等任务。 我们的实验表明，在输出完全依赖于输入的领域中，模型输出质量评估非常准确，IO变压器在Change Dataset 25（CD25）上实现了完美的评估准确性。 我们还探索了修改后的Swin V2架构。 最终，Swin V2在IO分割数据集上的得分为95.41%，在输出不完全依赖于输入的情况下，其表现优于IO变压器。 我们的工作扩展了变压器架构在计算机视觉中的奖励建模应用，并为优化这些模型以适应各种任务提供了关键见解。 显示英文摘要

摘要： Transformers and their derivatives have achieved state-of-the-art performance across text, vision, and speech recognition tasks. However, minimal effort has been made to train transformers capable of evaluating the output quality of other models. This paper examines SwinV2-based reward models, called the Input-Output Transformer (IO Transformer) and the Output Transformer. These reward models can be leveraged for tasks such as inference quality evaluation, data categorization, and policy optimization. Our experiments demonstrate highly accurate model output quality assessment across domains where the output is entirely dependent on the input, with the IO Transformer achieving perfect evaluation accuracy on the Change Dataset 25 (CD25). We also explore modified Swin V2 architectures. Ultimately Swin V2 remains on top with a score of 95.41 % on the IO Segmentation Dataset, outperforming the IO Transformer in scenarios where the output is not entirely dependent on the input. Our work expands the application of transformer architectures to reward modeling in computer vision and provides critical insights into optimizing these models for various tasks.