Imagine a single AI model that can identify fire risks from satellite images, classify galaxies, and even predict someone's age from a photo. This isn't science fiction—it's the promise of multi-task learning in computer vision. Researchers are exploring innovative ways to merge smaller, specialized AI models into a single powerhouse. Traditional methods of training large AI models for multiple tasks are computationally expensive and time-consuming. This new research tackles this challenge by using a clever technique called 'LoRA adapters.' Think of these adapters as specialized modules that can be plugged into a larger base model, giving it the ability to perform a specific task. The researchers experimented with merging these adapters, trained on different vision datasets, to create a single model capable of handling multiple tasks. They used a variety of datasets, including satellite images for fire risk assessment, galaxy images for classification, and facial images for age and emotion recognition. The results are intriguing. While merging adapters does lead to a slight performance dip in some cases, the multi-task models still outperformed the traditional approach of fine-tuning a single large model. Interestingly, adapters trained on very different datasets, like satellite images and faces, worked better together than adapters trained on similar data. This suggests that the models are learning complementary features. This research opens exciting possibilities for building more efficient and versatile AI systems. Imagine a future where drones use a single model to navigate, identify objects, and analyze environmental conditions. Or a medical imaging system that can diagnose multiple conditions at once. While challenges remain, such as minimizing the performance drop after merging, this research provides a significant step towards a future of more powerful and flexible AI in computer vision.