Deep learning models have proven to be successful in a wide <br>range of machine learning tasks. Yet, they are often highly sensitive to <br>perturbations on the input data which can lead to incorrect decisions <br>with high confidence, hampering their deployment for practical <br>use-cases. Thus, finding architectures that are (more) robust against <br>perturbations has received much attention in recent years. Just like the <br>search for well-performing architectures in terms of clean accuracy, <br>this usually involves a tedious trial-and-error process with one <br>additional challenge: the evaluation of a network's robustness is <br>significantly more expensive than its evaluation for clean accuracy. <br>Thus, the aim of this paper is to facilitate better streamlined research <br>on architectural design choices with respect to their impact on <br>robustness as well as, for example, the evaluation of surrogate measures <br>for robustness. We therefore borrow one of the most commonly considered <br>search spaces for neural architecture search for image classification, <br>NAS-Bench-201, which contains a manageable size of 6466 non-isomorphic <br>network designs. We evaluate all these networks on a range of common <br>adversarial attacks and corruption types and introduce a database on <br>neural architecture design and robustness evaluations. We further <br>present three exemplary use cases of this dataset, in which we (i) <br>benchmark robustness measurements based on Jacobian and Hessian matrices <br>for their robustness predictability, (ii) perform neural architecture <br>search on robust accuracies, and (iii) provide an initial analysis of <br>how architectural design choices affect robustness. We find that <br>carefully crafting the topology of a network can have substantial impact <br>on its robustness, where networks with the same parameter count range in <br>mean adversarial robust accuracy from 20%-41%.

Human performance capture is a highly important computer vision problem with<br>many applications in movie production and virtual/augmented reality. Many<br>previous performance capture approaches either required expensive multi-view<br>setups or did not recover dense space-time coherent geometry with<br>frame-to-frame correspondences. We propose a novel deep learning approach for<br>monocular dense human performance capture. Our method is trained in a weakly<br>supervised manner based on multi-view supervision completely removing the need<br>for training data with 3D ground truth annotations. The network architecture is<br>based on two separate networks that disentangle the task into a pose estimation<br>and a non-rigid surface deformation step. Extensive qualitative and<br>quantitative evaluations show that our approach outperforms the state of the<br>art in terms of quality and robustness. This work is an extended version of<br>DeepCap where we provide more detailed explanations, comparisons and results as<br>well as applications.<br>

Minimum cost lifted multicut problem is a generalization of the multicut problem and is a means to optimizing a decomposition of a graph w.r.t. both positive and negative edge costs. Its main advantage is that multicut-based formulations do not require the number of components given a priori; instead, it is deduced from the solution. However, the standard multicut cost function is limited to pairwise relationships between nodes, while several important applications either require or can benefit from a higher-order cost function, i.e. hyper-edges. In this paper, we propose a pseudo-boolean formulation for a multiple model fitting problem. It is based on a formulation of any-order minimum cost lifted multicuts, which allows to partition an undirected graph with pairwise connectivity such as to minimize costs defined over any set of hyper-edges. As the proposed formulation is NP-hard and the branch-and-bound algorithm is too slow in practice, we propose an efficient local search algorithm for inference into resulting problems. We demonstrate versatility and effectiveness of our approach in several applications: geometric multiple model fitting, homography and motion estimation, motion segmentation.