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.

我们的研究专注于分析和改进基于图的关系推理变形器（GRIT），这是该领域的一个重要基准。我们使用 PISC-fine 数据集进行了全面的消融研究，以寻找和探索 GRITv2 的效率和性能改进。我们的研究在 PISC 关系数据集上提供了一个新的冠军关系识别模型。我们在 GRIT 模型中引入了几个特性，并对我们的新基准进行了分析，分别为 GRITv2-L（大型）和 GRITv2-S（小型）。我们提出的 GRITv2-L 在关系识别上超过了现有方法，而 GRITv2-S 与 GRITv2-L 的性能差距仅为 2%，而其模型大小和参数仅为 GRITv2-L 的 0.0625 倍。此外，我们还解决了模型压缩的需求，这是在资源受限平台上部署高效模型的关键领域。通过应用量化技术，我们将 GRITv2-S 的大小有效地减小到 22MB，并在旗舰手机 OnePlus 12 上部署，仍然超越 PISC-fine 基准，突显了我们的模型在移动设备上的实用性和改进的高效性。