Improving Viewpoint Robustness for Visual Recognition via Adversarial Training

Visual recognition systems struggle when objects are viewed from different angles, even though the object hasn't changed. This paper proposes Viewpoint-Invariant Adversarial Training (VIAT), which treats different viewing angles as attacks and trains AI models to handle them better by learning from a distribution of challenging viewpoints. The researchers also created new datasets and evaluation methods to measure how well vision models can handle viewpoint changes.