Estimation of Gait Relative Attribute Distributions using a Differentiable Trade-off Model of Optimal and Uniform Transports

This paper describes a method for estimating gait relative attribute distributions. Existing datasets for gait relative attributes have only three-grade annotations, which cannot be represented in the form of distributions. Thus, we first create a dataset with seven-grade annotations for five gait relative attributes (i.e., beautiful, graceful, cheerful, imposing, and relaxed). Second, we design a deep neural network to handle gait relative attribute distributions. Although the ground-truth (i.e., annotation) is given in a relative (or pairwise) manner with some degree of uncertainty (i.e., inconsistency among multiple annotators), it is desirable for the system to output an absolute attribute distribution for each gait input. Therefore, we develop a model that converts a pair of absolute attribute distributions into a relative attribute distribution. More specifically, we formulate the conversion as a transportation process from one absolute attribute distribution to the other, then derive a differentiable model that determines the trade-off between optimal transport and uniform transport. Finally, we learn the network parameters by minimizing the dissimilarity between the estimated and ground-truth distributions through the Kullback–Leibler divergence and the expectation dissimilarity. Experimental results show that the proposed method successfully estimates both absolute and relative attribute distributions.