The blending of linguistic and visual information represents an emerging field in Artificial Intelligence (AI). As multimodal models evolve, they offer new ways for machine comprehension to interact with visual and textual data. This step beyond the traditional capacity of large language models (LLMs) involves creating detailed image captions and responding accurately to visual questions.
Integrating…
Introducing VisionGPT-3D: Combining Top-tier Vision Models for Creating 3D Structures from 2D Images
The fusion of text and visual components has transformed daily routines, such as image generation and element identification. While past computer vision models focused on object detection and categorization, larger language models like OpenAI GPT-4 have bridged the gap between natural language and visual representation. Although models like GPT-4 and SORA have made significant strides,…
Researchers from Massachusetts Institute of Technology (MIT) have developed the Texture Tiling Model (TTM), a technique intended to address issues faced when attempting to model human visual perception accurately within deep neural networks (DNNs), and particularly peripheral vision. This area of vision, which views the world with less fidelity further away from the focal center,…
Image Restoration (IR) is a key aspect of computer vision that aims to retrieve high-quality images from their degraded versions. Traditional techniques have made significant progress in this area; however, they have recently been outperformed by Diffusion Models, a technique that's emerging as a highly effective method in image restoration. Yet, existing Diffusion Models often…
As software companies grow, their codebases often become more complex, resulting in accumulated legacy code and technical debt. This situation becomes more challenging when team members - especially those well-versed in the codebase - leave the company. Newer team members may face difficulties understanding the code due to outdated or missing documentation. To overcome these…
Large Language Models (LLMs) have significantly impacted machine learning and natural language processing, with Transformer architecture being central to this progression. Nonetheless, LLMs have their share of challenges, notably dealing with lengthy sequences. Traditional attention mechanisms are known to increase the computational and memory costs quadratically in relation to sequence length, making processing long sequences…
NVIDIA is pushing boundaries in the world of AI and high-performance computing (HPC) with the launch of its Blackwell platform. Named after renowned mathematician, David Harold Blackwell, the platform introduces two innovative Graphics Processing Units (GPUs) – the B100 and the B200 – which promise to shake up AI and HPC with groundbreaking advancements.
The B100…
Large language models (LLMs) have made significant strides in the field of artificial intelligence, paving the way for machines that understand and generate human-like text. However, these models face the inherent challenge of their knowledge being fixed at the point of their training, limiting their adaptability and ability to incorporate new information post-training. This proves…
Large Vision Language Models (LVLMs) have been successful in text and image comprehension tasks, including Referring Expression Comprehension (REC). Notably, models like Griffon have made significant progress in areas such as object detection, denoting a key improvement in perception within LVLMs. Unfortunately, known challenges with LVLMs include their inability to match task-specific experts in intricate…
In a recent AI research paper, Google researchers have developed a new pre-trained scorer model, named Cappy, which has been designed to improve and surpass the capabilities of large multi-task language models (LLMs). This new development aims to tackle the primary issues related to LLMs. While they demonstrate remarkable performance and compatibility with numerous natural…
Medical image segmentation is a key component in diagnosis and treatment, with UNet's symmetrical architecture often used to outline organs and lesions accurately. However, its convolutional nature requires assistance to capture global semantic information, thereby limiting its effectiveness in complex medical tasks. There have been attempts to integrate Transformer architectures to address this, but these…