Referring Image Segmentation (RIS), aims to segment the object referred by a given sentence in an image by understanding both visual and linguistic information. However, existing RIS methods tend to explore top-performance models, disregarding considerations for practical applications on resources-limited edge devices. This oversight poses a significant challenge for on-device RIS inference. To this end, we propose an effective and efficient post-training quantization framework termed PTQ4RIS. Specifically, we first conduct an in-depth analysis of the root causes of performance degradation in RIS model quantization and propose dual-region quantization (DRQ) and reorder-based outlier-retained quantization (RORQ) to address the quantization difficulties in visual and text encoders. Extensive experiments on three benchmarks with different bits settings (from 8 to 4 bits) demonstrates its superior performance. Importantly, we are the first PTQ method specifically designed for the RIS task, highlighting the feasibility of PTQ in RIS applications. Code will be available at {https://github.com/gugu511yy/PTQ4RIS}.

本文针对现有的参考图像分割方法在资源受限的边缘设备上的应用不足提出了研究空白。我们提出了一种名为PTQ4RIS的后训练量化框架，通过双区域量化和基于重排的异常保留量化技术，有效解决了视觉和文本编码器的量化问题。实验结果显示该方法在不同位数设置下的性能优越，证明了后训练量化在参考图像分割任务中的可行性。

PTQ4RIS：面向参考图像分割的后训练量化