Computer Vision

Vision-language models (VLMs) play a crucial role in today’s intelligent systems by enabling a detailed understanding of visual content. The complexity of multimodal intelligence tasks has grown, ranging from scientific problem-solving to the development of autonomous agents. Current demands on VLMs have far exceeded simple visual content perception, with increasing attention on advanced reasoning. While…

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…

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…