PTQ4RIS: Post-Training Quantization for Referring Image Segmentation
<|reference_start|>PTQ4RIS: Post-Training Quantization for Referring Image Segmentation: 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}.<|reference_end|>
@article{jiang2024ptq4ris:,
    title={PTQ4RIS: Post-Training Quantization for Referring Image Segmentation},
    author={Xiaoyan Jiang, Hang Yang, Kaiying Zhu, Xihe Qiu, Shibo Zhao, Sifan
  Zhou},
    journal={arXiv preprint arXiv:2409.17020},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.17020},
    primaryClass={cs.CV}
}