Video Foundation Model

Video foundation models (VFMs) aim to learn general-purpose representations for diverse video understanding tasks, moving beyond task-specific models. Current research emphasizes improving the robustness and efficiency of VFMs, focusing on architectures like masked autoencoders and transformer-based models, and exploring effective pre-training strategies including contrastive learning and generative approaches. This work is significant because it enables more accurate and efficient video analysis across various applications, from action recognition and video-text retrieval to robotic learning and general video understanding. The development of more generalizable and efficient VFMs is a key area of advancement in computer vision.