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.…

Vision Language Models (VLMs) allow both text inputs and visual understanding. However, image resolution is crucial for VLM performance for processing text and chart-rich data. Increasing image resolution creates significant challenges. First, pretrained vision encoders often struggle with high-resolution images due to inefficient pretraining requirements. Running inference on high-resolution images increases computational costs and latency…

Advancements in artificial intelligence are rapidly closing the gap between digital reasoning and real-world interaction. At the forefront of this progress is embodied AI—the field focused on enabling robots to perceive, reason, and act effectively in physical environments. As industries look to automate complex spatial and temporal tasks—from household assistance to logistics—having AI systems that…

Autoregressive video generation is a rapidly evolving research domain. It focuses on the synthesis of videos frame-by-frame using learned patterns of both spatial arrangements and temporal dynamics. Unlike traditional video creation methods, which may rely on pre-built frames or handcrafted transitions, autoregressive models aim to generate content dynamically based on prior tokens. This approach is…

Bridging the Gap Between Artistic Intent and Technical Execution Photo retouching is a core aspect of digital photography, enabling users to manipulate image elements such as tone, exposure, and contrast to create visually compelling content. Whether for professional purposes or personal expression, users often seek to enhance images in ways that align with specific aesthetic…

Understanding the Link Between Body Movement and Visual Perception The study of human visual perception through egocentric views is crucial in developing intelligent systems capable of understanding & interacting with their environment. This area emphasizes how movements of the human body—ranging from locomotion to arm manipulation—shape what is seen from a first-person perspective. Understanding this…

Key Takeaways: Researchers from Google DeepMind, the University of Michigan & Brown university have developed “Motion Prompting,” a new method for controlling video generation using specific motion trajectories. The technique uses “motion prompts,” a flexible representation of movement that can be either sparse or dense, to guide a pre-trained video diffusion model. A key innovation…