AI News

Optical Character Recognition (OCR) is the process of turning images that contain text—such as scanned pages, receipts, or photographs—into machine-readable text. What began as brittle rule-based systems has evolved into a rich ecosystem of neural architectures and vision-language models capable of reading complex, multi-lingual, and handwritten documents. How OCR Works? Every OCR system tackles three…

Introduction Understanding how the brain builds internal representations of the visual world is one of the most fascinating challenges in neuroscience. Over the past decade, deep learning has reshaped computer vision, producing neural networks that not only perform at human-level accuracy on recognition tasks but also seem to process information in ways that resemble our…

In the domain of multimodal AI, instruction-based image editing models are transforming how users interact with visual content. Just released in August 2025 by Alibaba’s Qwen Team, Qwen-Image-Edit builds on the 20B-parameter Qwen-Image foundation to deliver advanced editing capabilities. This model excels in semantic editing (e.g., style transfer and novel view synthesis) and appearance editing…

Meta AI has just released DINOv3, a breakthrough self-supervised computer vision model that sets new standards for versatility and…

Multimodal reasoning, where models integrate and interpret information from multiple sources such as text, images, and diagrams, is a frontier challenge in AI. VL-Cogito is a state-of-the-art Multimodal Large Language Model (MLLM) proposed by DAMO Academy (Alibaba Group) and partners, introducing a robust reinforcement learning pipeline that fundamentally upgrades the reasoning skills of large models…

Introduction Galileo is an open-source, highly multimodal foundation model developed to process, analyze, and understand diverse Earth observation (EO) data streams—including optical, radar, elevation, climate, and auxiliary maps—at scale. Galileo is developed with the support from researchers from McGill University, NASA Harvest Ai2, Carleton University, University of British Columbia, Vector Institute, and Arizona State University.…

Embedding models act as bridges between different data modalities by encoding diverse multimodal information into a shared dense representation space. There have been advancements in embedding models in recent years, driven by progress in large foundation models. However, existing multimodal embedding models are trained on datasets such as MMEB and M-BEIR, with most focus only…

Introduction to Video Diffusion Models and Computational Challenges Diffusion models have made impressive progress in generating high-quality, coherent videos, building on their success in image synthesis. However, handling the extra temporal dimension in videos significantly increases computational demands, especially since self-attention scales poorly with sequence length. This makes it difficult to train or run these…

Why Multimodal Reasoning Matters for Vision-Language Tasks Multimodal reasoning enables models to make informed decisions and answer questions by combining both visual and textual information. This type of reasoning plays a central role in interpreting charts, answering image-based questions, and understanding complex visual documents. The goal is to make machines capable of using vision as…

LLMs have made significant strides in language-related tasks such as conversational AI, reasoning, and code generation. However, human communication extends beyond text, often incorporating visual elements to enhance understanding. To create a truly versatile AI, models need the ability to process and generate text and visual information simultaneously. Training such unified vision-language models from scratch…