The field of computer vision is moving towards developing more robust and invariant feature detection and description methods. Recent works have focused on leveraging attention mechanisms and deformable transformers to capture global context and geometric invariance. This has led to significant improvements in tasks such as structure-from-motion and SLAM. Additionally, there is a growing interest in developing face recognition systems that are robust to various degradations, including low-quality images and pose variations. Researchers are exploring novel approaches, including deformation-aware robustness and pose-invariant feature learning, to address these challenges. Noteworthy papers in this area include:

• RDD, which proposes a robust keypoint detector and descriptor using deformable transformers,
• DArFace, which introduces a deformation-aware robust face recognition framework,
• 2D-3D Attention and Entropy, which proposes a domain adaptive framework for pose robust 2D facial recognition, and
• Test-Time Augmentation for Pose-invariant Face Recognition, which enhances face recognition performance by augmenting head poses during the testing phase.