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Zero-shot capabilities, the ability to identify objects without prior training, present a potential game-changer for MR systems. However, these abilities can be limited in specific domains, leading to recommendations for fine-tuning the model for optimal performance. This paper presents a method to fine-tune the LMM for MR systems, focusing on improving object detection and recognition in diverse environments. This approach is demonstrated in a case study involving object detection in MR environments, a domain where foundational models typically do not perform well. Results show significant improvements in the performance of the MR system, with the fine-tuned LMM demonstrating superior object detection and recognition capabilities. This research opens up new possibilities for the application of zero-shot models in MR, paving the way for more immersive, interactive, and accurate mixed reality experiences. The implications of this research extend beyond MR, offering insights into how zero-shot models can be optimized for various specific domains. |
