Perspective Supervision

Perspective supervision in computer vision aims to improve model training by leveraging information from different viewpoints or representations. Current research focuses on using weaker forms of supervision, such as epipolar geometry or collaborative supervision from both image and feature spaces, to reduce reliance on strong, fully labeled datasets. This approach is particularly valuable in tasks like semantic segmentation and bird's-eye-view object detection, where it enables faster convergence and better adaptation of modern image backbones, leading to improved accuracy and efficiency. The resulting advancements promise to enhance the robustness and generalizability of computer vision models across diverse applications.