Underwater image processing combined with machine learning offers significant potential for enhancing the capabilities of underwater robots across various marine exploration tasks. Image segmentation, a key aspect of machine vision, is crucial for identifying and...

A significant challenge with question-answering (QA) systems in Natural Language Processing (NLP) is their performance in scenarios involving extensive collections of documents that are structurally similar or ‘indistinguishable.’ Traditional models often need help to...

The utility of large language models (LLMs) has been increasingly recognized, demonstrating remarkable capabilities in processing and interpreting vast datasets. These models have been instrumental in various tasks, from facilitating clinical trial matches to enabling...

According to recent studies, a policy’s depiction can significantly affect learning performance. Policy representations such as feed-forward neural networks, energy-based models, and diffusion have all been investigated in earlier research. A recent study by Carnegie...

MoD-SLAM is a state-of-the-art method for Simultaneous Localization And Mapping (SLAM) systems. In SLAM systems, it is challenging to achieve real-time, accurate, and scalable dense mapping. To address these challenges, researchers have introduced a novel method...

The task of view synthesis is essential in both computer vision and graphics, enabling the re-rendering of scenes from various viewpoints akin to the human eye. This capability is vital for everyday tasks and fosters creativity by allowing the envisioning and crafting...