While VLMs are strong at understanding both text and images, they often rely solely on text when reasoning, limiting their ability to solve tasks that require visual thinking, such as spatial puzzles. People naturally visualize solutions rather than describing every detail, but VLMs struggle to do the same. Although some recent models can generate both…
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…
Large multimodal models (LMMs) enable systems to interpret images, answer visual questions, and retrieve factual information by combining multiple modalities. Their development has significantly advanced the capabilities of virtual assistants and AI systems used in real-world settings. However, even with massive training data, LMMs often overlook dynamic or evolving information, especially facts that emerge post-training…
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…
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…
Navigating the dense urban canyons of cities like San Francisco or New York can be a nightmare for GPS systems. The towering skyscrapers block and reflect satellite signals, leading to location errors of tens of meters. For you and me, that might mean a missed turn. But for an autonomous vehicle or a delivery robot,…
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…