Computer Vision Problem

Computer vision research focuses on enabling computers to "see" and interpret images and videos, aiming to replicate human visual perception. Current efforts concentrate on improving the accuracy and robustness of models, particularly through deep learning architectures like Vision Transformers and the application of transfer learning to reduce data requirements and computational costs. This field is crucial for advancements in robotics, augmented reality, medical imaging, and numerous other applications, driving progress in areas such as object pose estimation, structure-from-motion, and image retrieval. Furthermore, research explores novel approaches like quantum computing and the integration of language models to enhance capabilities and address limitations of existing methods.