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FinalMLP: A Simple yet Powerful Two-Stream MLP Model for Recommendation Systems

Discover how FinalMLP transforms online recommendations: unlocking personalized experiences with cutting-edge AI research This post was co-authored with Rafael Guedes. The world has been evolving towards a digital era where everyone has nearly everything they want at a click of distance. These benefits of accessibility, comfort, and a large quantity of offers come with new…

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Researchers from UT Austin and AWS AI Introduce a Novel AI Framework ‘ViGoR’ that Utilizes Fine-Grained Reward Modeling to Significantly Enhance the Visual Grounding of LVLMs over Pre-Trained Baselines

Integrating natural language understanding with image perception has led to the development of large vision language models (LVLMs), which showcase remarkable reasoning capabilities. Despite their progress, LVLMs often encounter challenges in accurately anchoring generated text to visual inputs, manifesting as inaccuracies like hallucinations of non-existent scene elements or misinterpretations of object attributes and relationships. Researchers…

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EfficientViT-SAM: A New Family of Accelerated Segment Anything Models

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…

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