Looking ahead, Imad outlines two key future directions for this work. First, there is an interest in streamlining the iterative process involved in finding the optimal model configuration, aiming for a more efficient and straightforward approach. Second, the plan is to extend the evaluation of the method to additional computer vision tasks, such as semantic segmentation and object detection. “With the image classification task, we’ve noticed that, depending on the downstream task you want to adapt your model on, it will adapt certain layers and discard the others,” he points out. “We’d like to find a way to remove the iterative process because, in our approach, it’s an iterative process to find the right configuration for the model.” Imad’s enthusiasm for this work stems from living in an era where we have really large models at our disposal, which are excellent in general but sometimes need improvement concerning specific tasks. “I’d like to do this in an efficient way using less computational power and also get really good performance,” he tells us. “I like working on computer vision in general. It’s really interesting. That’s why I’m focusing mainly on computer vision tasks.” 13 DAILY WACV Friday Mini but Mighty
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