Computer vision research focuses on improving accuracy in low-light and adverse conditions. Current work addresses challenges like motion blur, poor lighting, and atmospheric interference. Key areas include developing robust algorithms for night vision systems, underwater imaging, and through-fog detection.
Projects often combine traditional image processing with deep learning to enhance performance.Multi-modal learning combines visual data with other input types like text, audio, or sensor data. Research explores how to fuse different data sources effectively to improve understanding. Examples include systems that can generate images from text descriptions, understand visual references in conversations, or combine thermal and visible light images for better object detection.
Real-time 3D scene understanding remains an active research area. This includes accurate depth estimation from single images, dynamic scene reconstruction, and understanding object interactions in 3D space. Current work focuses on reducing computational requirements while maintaining accuracy, making these systems practical for mobile devices and autonomous vehicles.