Efficient Use of Computer Vision Technology in Post-Production of Film and Television

The increasingly complex tasks of shot editing, 3D modeling, and special effects synthesis are becoming more and more difficult, and the traditional way of relying on manual experience has not been able to meet the dual requirements of high efficiency and high precision. In recent years, computer vision technology has been gradually introduced into film and television post-production because of its powerful abilities in target recognition, image segmentation, and dynamic tracking. This paper proposes an innovative multi-model collaborative framework that integrates convolutional networks, attention mechanisms, and feedback optimization. The experimental results show that this method is significantly superior to the traditional method in processing speed, automation degree, and output quality, which effectively reduces the manual intervention rate and improves the stability and scalability of the system. Research shows that computer vision not only greatly improves the post-production efficiency but also promotes the deep integration of artistic expression and technology.