Occlusion Sensitivity Analysis

Occlusion sensitivity analysis investigates how the presence or absence of occluded regions in images or point clouds affects the performance of computer vision models. Current research focuses on developing methods to improve model robustness to occlusions, employing techniques like generative adversarial networks (GANs) for occlusion removal, reinforcement learning for occlusion-aware decision-making, and transformer networks for integrating visual and geometric information to infer occluded regions. This work is crucial for enhancing the reliability and accuracy of computer vision systems in real-world applications, such as autonomous driving, robotics, and medical image analysis, where occlusions are frequently encountered. The ultimate goal is to create more robust and interpretable models that can handle incomplete or partially obscured data effectively.