Our research focuses on the analysis and improvement of the Graph-based Relation Inference Transformer (GRIT), which serves as an important benchmark in the field. We conduct a comprehensive ablation study using the PISC-fine dataset, to find and explore improvement in efficiency and performance of GRITv2. Our research has provided a new state-of-the-art relation recognition model on the PISC relation dataset. We introduce several features in the GRIT model and analyse our new benchmarks in two versions: GRITv2-L (large) and GRITv2-S (small). Our proposed GRITv2-L surpasses existing methods on relation recognition and the GRITv2-S is within 2% performance gap of GRITv2-L, which has only 0.0625x the model size and parameters of GRITv2-L. Furthermore, we also address the need for model compression, an area crucial for deploying efficient models on resource-constrained platforms. By applying quantization techniques, we efficiently reduced the GRITv2-S size to 22MB and deployed it on the flagship OnePlus 12 mobile which still surpasses the PISC-fine benchmarks in performance, highlighting the practical viability and improved efficiency of our model on mobile devices.

翻译：我们的研究聚焦于图基关系推理Transformer（GRIT）的分析与改进——该模型是该领域的重要基准。通过使用PISC-fine数据集进行全面的消融研究，我们探索并发现了GRITv2在效率与性能上的改进方向。本研究在PISC关系数据集上提出了新的最优关系识别模型。我们在GRIT模型中引入多项特性，并以GRITv2-L（大型）和GRITv2-S（小型）两个版本分析新基准。我们提出的GRITv2-L在关系识别上超越了现有方法，而GRITv2-S与GRITv2-L的性能差距仅在2%以内，但其模型尺寸与参数量仅为GRITv2-L的0.0625倍。此外，我们还解决了模型压缩这一关键需求——该方向对在资源受限平台上部署高效模型至关重要。通过应用量化技术，我们将GRITv2-S的模型大小高效压缩至22MB，并将其部署在旗舰级OnePlus 12手机上，该模型在PISC-fine基准测试中的表现仍保持领先，凸显了本模型在移动设备上的实际可行性与效率提升。