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3D computer vision has gained immense traction recently due to its robotics, augmented reality, and virtual reality applications. These technologies demand an extensive amount of high-quality 3D data to function effectively. However, acquiring such data is inherently complex, requiring specialized equipment, expert knowledge, and significant time investments. Unlike 2D data, which is relatively easier to…
Retrieval-augmented generation (RAG) has emerged as a prominent application in the field of natural language processing. This innovative approach involves breaking down large documents into smaller, manageable text chunks, typically limited to around 512 tokens. These bite-sized pieces of information are then stored in a vector database, with each chunk represented by a unique vector…
Document ranking remains one of the most important issues in information retrieval & natural language processing development. Effective document retrieval and ranking are highly important in enhancing the performance of search engines, question-answering systems, and Retrieval-Augmented Generation (RAG) systems. Traditional ranking models often need help finding a good balance between the precision of results and…
Digital Twin (DT) technology is becoming more and more popular as a method that gives Internet of Things (IoT) devices dynamic topology mapping and real-time status updates. However, there are difficulties in deploying DT in industrial IoT networks, especially when significant and dispersed data support is required. This frequently results in the creation of data…
The main focus of existing Multimodal Large Language Models (MLLMs) is on individual image interpretation, which restricts their ability to tackle tasks involving many images. These challenges demand models to comprehend and integrate information across several images, including Knowledge-Based Visual Question Answering (VQA), Visual Relation Inference, and Multi-image Reasoning. The majority of current MLLMs struggle…
This paper introduces Show-o, a unified transformer model that integrates multimodal understanding and generation capabilities within a single architecture. As artificial intelligence advances, there’s been significant progress in multimodal understanding (e.g., visual question-answering) and generation (e.g., text-to-image synthesis) separately. However, unifying these capabilities in one model remains a challenge. Show-o addresses this by innovatively combining…
Data analysis helps organizations make informed decisions by turning raw data into actionable insights. With businesses increasingly relying on data-driven strategies, the demand for skilled data analysts is rising. Learning data analysis equips you with the tools to uncover trends, solve problems, and add value in any field. This article lists the top data analysis…
The quantity and quality of data directly impact the efficacy and accuracy of AI models. Getting accurate and pertinent data is one of the biggest challenges in the development of AI. LLMs require current, high-quality internet data to address certain issues. It is challenging to compile data from the internet. Coordinating crawlers, locating interesting pages…
Trustworthiness reasoning in multiplayer games with incomplete information presents significant challenges. Players need to assess the reliability of others based on partial, often misleading information while making decisions in real time. Traditional approaches, heavily reliant on pre-trained models, struggle to adapt to dynamic environments due to their dependence on domain-specific data and feedback rewards. These…
With speech-to-speech technology, the focus has shifted toward more prominent facilitation of spoken language toward other spoken outputs, enabling better communication and access within diverse applications. This ranges from voice recognition to language processing and speech synthesis. These elements, combined with the speech-to-speech systems, would work toward making such an experience seamless, one that works…