The insights and techniques from PTQ4RIS can be effectively applied to other multi-modal computer vision tasks, such as visual question answering (VQA) and image captioning. In VQA, the model must integrate visual features with textual questions, similar to how PTQ4RIS combines visual and linguistic information for referring image segmentation. The dual-region quantization (DRQ) approach could be adapted to handle the unique activation distributions that arise from processing both images and questions, ensuring that quantization does not compromise the model's ability to understand complex queries. Similarly, the reorder-based outlier-retained quantization (RORQ) method could be utilized to manage the diverse range of activations generated by different question types, enhancing the model's robustness against quantization errors. For image captioning, the techniques could be employed to optimize the model's performance while maintaining the ability to generate coherent and contextually relevant descriptions. By leveraging the quantization strategies developed in PTQ4RIS, other multi-modal tasks can achieve improved efficiency and performance, facilitating deployment on resource-constrained devices while maintaining high accuracy.