Occlusion Augmentation

Occlusion augmentation is a technique used to improve the robustness of computer vision models by artificially introducing occlusions—obstructions blocking parts of an image—during training. Current research focuses on developing sophisticated augmentation strategies, often leveraging recent advances in segmentation models, to generate realistic occlusions and integrating these techniques into various model architectures, including transformers and graph convolutional networks. This approach is crucial for enhancing the performance of computer vision systems in real-world scenarios where occlusions are common, impacting applications such as object detection, pose estimation, and person re-identification. The resulting models demonstrate improved accuracy and reliability in handling partially obscured objects or individuals.