Vision Compression

Vision compression aims to reduce the computational burden and storage requirements of visual data, enabling efficient processing of high-resolution images and videos for various applications. Current research focuses on integrating compression directly into deep learning models, such as leveraging large language models to learn efficient representations of visual information or employing a priori compression techniques within convolutional neural networks. These advancements are crucial for scaling up video understanding tasks, particularly those involving long videos, and for deploying complex vision models on resource-constrained devices.