Muhammad Uzair Khattak

Title: Muhammad Uzair Khattak: Innovator in Machine Learning and Vision-Language Models

Introduction

Muhammad Uzair Khattak is a prominent inventor based in Abu Dhabi, AE. He has made significant contributions to the field of artificial intelligence, particularly in machine learning and vision-language models. With a total of 4 patents, his work is paving the way for advancements in image recognition tasks.

Latest Patents

One of his latest patents is a language machine learning model for image recognition tasks. This invention involves a computer-readable storage medium and a method for adapting a vision-language (VL) machine learning model from a pre-trained VL model. The pre-trained VL model includes a pre-trained image encoder and a pre-trained textual encoder. The method is applicable to few-shot learning and training of learnable prompts. After feature extraction, a machine learning engine determines a cross-entropy loss from prompted image features and prompted textual features. The machine learning engine then determines a self-regularizing consistency loss from the prompted image features, pre-trained image features, the prompted textual features, and pre-trained textual features. The learnable text and image prompts are updated based on the cross-entropy loss in combination with the self-regularizing consistency loss. To preserve generalization for downstream tasks, the updating with the self-regularizing consistency loss is constrained to maximize mutual agreement between the prompted image and textual features and the pre-trained image and textual features.

Another notable patent is focused on multi-modal prompt learning for representation transfer on image recognition tasks. This method and system for multi-modal prompt learning of vision-language models allows encodings of image-text pairs to be combined with image prompts and text prompts before being input into an image encoder and text encoder of a vision-language model respectively. The image prompt can be generated using the text prompt through a vision-language coupling function to encourage synergy between the two prompts. The combination of encodings and prompts can be fed through the transformer layers of the encoders, and the output of each layer can be combined with a new prompt before entering the next layer, up until a specific depth. The subsequent transformer layers can process the output and generate a final representation for the image and text, which can then be used for downstream tasks.

Career Highlights

Muhammad Uzair Khattak is currently associated with Mohamed Bin Zayed University of Artificial Intelligence. His work at this institution emphasizes the importance of