A probabilistic segmentation scheme

Abstract. We propose a probabilistic segmentation scheme, which is widely applicable to some extend. Besides the segmentation itself our model incorporates object specific shading. Dependent upon application, the latter is interpreted either as a perturbation or as meaningful object characteristic. We discuss the recognition task for segmentation, learning tasks for parameter estimation as well as different formulations of shading estimation tasks

Modelling composite shapes by Gibbs random fields

We analyse the potential of Gibbs Random Fields for shape prior modelling. We show that the expressive power of second order GRFs is already sufficient to express spatial relations between shape parts and simple shapes simultane-ously. This allows to model and recognise complex shapes as spatial compositions of simpler parts. 1

Enhancing Fairness of Visual Attribute Predictors

The performance of deep neural networks for image recognition tasks such as predicting a smiling face is known to degrade with under-represented classes of sensitive attributes. We address this problem by introducing fairness-aware regularization losses based on batch estimates of Demographic Parity, Equalized Odds, and a novel Intersection-over-Union measure. The experiments performed on facial and medical images from CelebA, UTKFace, and the SIIM-ISIC melanoma classification challenge show the effectiveness of our proposed fairness losses for bias mitigation as they improve model fairness while maintaining high classification performance. To the best of our knowledge, our work is the first attempt to incorporate these types of losses in an end-to-end training scheme for mitigating biases of visual attribute predictors. Our code is available at https://github.com/nish03/FVAP.Comment: Camera Ready, ACCV 202

M-Best-Diverse Labelings for Submodular Energies and Beyond

Abstract We consider the problem of finding M best diverse solutions of energy minimization problems for graphical models. Contrary to the sequential method of Batra et al., which greedily finds one solution after another, we infer all M solutions jointly. It was shown recently that such jointly inferred labelings not only have smaller total energy but also qualitatively outperform the sequentially obtained ones. The only obstacle for using this new technique is the complexity of the corresponding inference problem, since it is considerably slower algorithm than the method of Batra et al. In this work we show that the joint inference of M best diverse solutions can be formulated as a submodular energy minimization if the original MAP-inference problem is submodular, hence fast inference techniques can be used. In addition to the theoretical results we provide practical algorithms that outperform the current state-of-the-art and can be used in both submodular and non-submodular case

Transforming an arbitrary minsum problem into a binary one

ABSTRACT. In this report we show, that an arbitrary MinSum problem (i.e. a MinSum problem with an arbitrary finite set of states) can be adequately transformed into a binary one (i.e. into a MinSum problem with only two states). Consequently all known results for binary MinSum problems can be easily extended to the general case. For instance it gives the possibility to solve exactly submodular MinSum problems with more than two states by using MinCut-MaxFlow based technics. CONTENT