Scalable Visual Representation

Scalable visual representation research aims to create efficient and effective methods for encoding and processing visual data at massive scales, crucial for applications like autonomous driving and e-commerce. Current efforts focus on developing novel architectures, such as masked image modeling and transformer-based models, that leverage both spatial and temporal information from various data sources (images, videos, point clouds) and incorporate techniques like continual learning and differential privacy to improve efficiency and robustness. These advancements enable improved performance in downstream tasks such as object detection, image classification, and graph generation, impacting fields ranging from computer vision to drug discovery.