Understanding Learned Visual Invariances Through Hierarchical Dataset Design and Collection

While CNNs have enabled tremendous progress in computer vision for a variety of tasks, robust generalization across domain, viewpoint, and class remains a significant challenge. This thesis therefore centers around a new hierarchical multiview, multidomain image dataset with 3D meshes called 3D-ODDS. Data was collected in several ways, involving turntable setups, flying drones, and in-the-wild photos under diverse indoor/outdoor locations. Experiments were subsequently conducted on two important vision tasks: single view 3D reconstruction and image classification. For single view 3D reconstruction, a novel postprocessing step involving test-time self-supervised learning is proposed to help improve reconstructed shape robustness. For image classification, we consider an adversarial attack framework using perturbations which are semantically imperceptible based on human subject surveys. For both tasks, experiments show that 3D-ODDS is a challenging dataset for state of the art methods and is useful in measuring class, pose, and domain invariance. We believe that the dataset will remain relevant moving forward, inspiring future works towards robust and invariant methods in computer vision