The landscape of image segmentation has been profoundly transformed by the introduction of the Segment Anything Model (SAM), a paradigm known for its remarkable zero-shot segmentation capability. SAM’s deployment across a wide array of applications, from augmented reality to data annotation, underscores its utility. However, SAM’s computational intensity, particularly its image encoder’s demand of 2973…

In artificial intelligence, integrating multimodal inputs for video reasoning stands as a frontier, challenging yet ripe with potential. Researchers increasingly focus on leveraging diverse data types – from visual frames and audio snippets to more complex 3D point clouds – to enrich AI’s understanding and interpretation of the world. This endeavor aims to mimic human…

The progress and development of artificial intelligence (AI) heavily rely on human evaluation, guidance, and expertise. In computer vision, convolutional networks acquire a semantic understanding of images through extensive labeling provided by experts, such as delineating object boundaries in datasets like COCO or categorizing images in ImageNet.  Similarly, in robotics, reinforcement learning often relies on…

Artificial intelligence has significantly advanced in developing systems that can interpret and respond to multimodal data. At the forefront of this innovation is Lumos, a groundbreaking multimodal question-answering system designed by researchers at Meta Reality Labs. Unlike traditional systems, Lumos distinguishes itself by its exceptional ability to extract and understand text from images, enhancing the…