No abstract

Visual relationship detection targets on predicting categories of predicates and object pairs, and also locating the object pairs. Recognizing the relationships between individual objects is important for describing visual scenes in static images. In this paper, we propose a novel end-to-end framework on the visual relationship detection task. First, we design a spatial attention model for specializing predicate features. Compared to a normal ROI-pooling layer, this structure significantly improves Predicate Classification performance. Second, for extracting relative spatial configuration, we propose to map simple geometric representations to a high dimension, which boosts relationship detection accuracy. Third, we implement a feature embedding model with a bi-directional RNN which considers subject, predicate and object as a time sequence. We evaluate our method on three tasks. The experiments demonstrate that our method achieves competitive results compared to state-of-the-art methods.

In this paper, we propose FairNN a neural network that performs joint feature representation and classification for fairness-aware learning. Our approach optimizes a multi-objective loss function in which (a) learns a fair representation by suppressing protected attributes (b) maintains the information content by minimizing a reconstruction loss and (c) allows for solving a classification task in a fair manner by minimizing the classification error and respecting the equalized odds-based fairness regularizer. Our experiments on a variety of datasets demonstrate that such a joint approach is superior to separate treatment of unfairness in representation learning or supervised learning. Additionally, our regularizers can be adaptively weighted to balance the different components of the loss function, thus allowing for a very general framework for conjoint fair representation learning and decision making.