A team of researchers from Peking University and Alibaba Group have introduced FastV, a model designed to mitigate the inefficiencies in computational processing within Large Vision-Language Models (LVLMs). In particular, FastV addresses the bias exhibited by the attention mechanism in LVLMs, which tends to favour textual tokens over visual tokens. Existing models - including LLaVA-1.5…
Today's increasingly pervasive artificial intelligence (AI) technologies have given rise to concerns over the perpetuation of historically entrenched human biases, particularly within marginalized communities. New research by academics from the Allen Institute for AI, Stanford University, and the University of Chicago exposes a worrying form of bias rarely discussed before: Dialect Prejudice against speakers of…
Recent advancements in large language models (LLMs), which have revolutionized fields like healthcare, translation, and code generation, are now being leveraged to assist the legal domain. Legal professionals often grapple with extensive, complex documents, emphasizing the need for a dedicated LLM. To address this, researchers from several prestigious institutions—including Equall.ai, MICS, CentraleSupélec, and Université Paris-Saclay—have…
Vision-Language Models (VLMs) provide state-of-the-art performance across a spectrum of vision-language tasks, including captioning, object localization, commonsense reasoning, and vision-based coding, amongst others. Recent studies, such as one undertaken by Apple, showed that these models excel in extracting text from images and interpreting visual data, including tables and charts. However, when tested on complex tasks…
Artificial Intelligence (AI) researchers have developed an innovative framework to produce visually and audibly cohesive content. This advancement could help overcome previous difficulties in synchronizing video and audio generation. The framework uses pre-trained models like ImageBind, which links different data types into a unified semantic space. This function allows ImageBind to provide feedback on the…
The 01.AI research team has introduced the Yi model family of Artificial Intelligence (AI) designed to bridge the gap between human language and visual perception. Uniquely, this model doesn't simply parse text or images individually; it combines both, demonstrating an unprecedented degree of multi-modal understanding. This ground-breaking technology's purpose is to mirror and extend human…
The boundary between the visual world and the realm of natural language has become a crucial frontier in the fast-changing field of artificial intelligence. Vision-language models, which aim to unravel the complicated relationship between images and text, are important developments for various applications, including enhancing accessibility and providing automated assistance in diverse industries.
However, creating models…
In the ever-evolving sphere of artificial intelligence, the study of large language models (LLMs) and how they interpret and process human language has provided valuable insights. Contrary to expectation, these innovative models represent concepts in a simple and linear manner. To demystify the basis of linear representations in LLMs, researchers from the University of Chicago…
A new multimodal system, created by scientists from the University of Waterloo and AWS AI Labs, uses text and images to create a more engaging and interactive user experience. The system, known as Multimodal Augmented Generative Images Dialogues (MAGID), improves upon traditional methods that have used static image databases or real-world sources, which can pose…
Computer vision traditionally concentrates on acknowledging universally agreed concepts like animals, vehicles, or specific objects. However, real-world applications often need to identify variable subjective concepts like predicting emotions, determining aesthetic appeal, or regulating content. What is considered "unsafe" content or "gourmet" food differs greatly among individuals, hence the increasing demand for user-centric training frameworks that…
Artificial Intelligence researchers are continuously striving to create models that can think, reason, and generate outputs similar to the way humans solve complex problems. However, Large Language Models (LLMs), the current best attempt at such a feat, often struggle to maintain factual accuracy, especially in tasks that require a series of logical steps. This lack…
The evolution of Multimodal Large Language Models (MLLMs) has been significant, particularly those models that blend language and vision modalities (LVMs). There has been growing interest in applying MLLMs in various fields like computer vision tasks and integrating them into complex pipelines.
Despite some models like ShareGPTV performing well in data annotation tasks, their practical…