Watch Your Pose: Unsupervised Domain Adaption with Pose based Triplet Selection for Gait Recognition

Gait Recognition is a computer vision task aiming to identify people by their walking patterns. Existing methods show impressive results on individual datasets but lack the ability to generalize to unseen scenarios. Unsupervised Domain Adaptation (UDA) tries to adapt a model, pre-trained in a supervised manner on a source domain, to an unlabelled target domain. UDA for Gait Recognition is still in its infancy and existing works proposed solutions to limited scenarios. In this paper, we reveal a fundamental phenomenon in adaptation of gait recognition models, in which the target domain is biased to pose-based features rather than identity features, causing a significant performance drop in the identification task. We suggest Gait Orientation-based method for Unsupervised Domain Adaptation (GOUDA) to reduce this bias. To this end, we present a novel Triplet Selection algorithm with a curriculum learning framework, aiming to adapt the embedding space by pushing away samples of similar poses and bringing closer samples of different poses. We provide extensive experiments on four widely-used gait datasets, CASIA-B, OU-MVLP, GREW, and Gait3D, and on three backbones, GaitSet, GaitPart, and GaitGL, showing the superiority of our proposed method over prior works.

Image Quality Assessment: Learning to Rank Image Distortion Level

Over the years, various algorithms were developed, attempting to imitate the Human Visual System (HVS), and evaluate the perceptual image quality. However, for certain image distortions, the functionality of the HVS continues to be an enigma, and echoing its behavior remains a challenge (especially for ill-defined distortions). In this paper, we learn to compare the image quality of two registered images, with respect to a chosen distortion. Our method takes advantage of the fact that at times, simulating image distortion and later evaluating its relative image quality, is easier than assessing its absolute value. Thus, given a pair of images, we look for an optimal dimensional reduction function that will map each image to a numerical score, so that the scores will reflect the image quality relation (i.e., a less distorted image will receive a lower score). We look for an optimal dimensional reduction mapping in the form of a Deep Neural Network which minimizes the violation of image quality order. Subsequently, we extend the method to order a set of images by utilizing the predicted level of the chosen distortion. We demonstrate the validity of our method on Latent Chromatic Aberration and Moire distortions, on synthetic and real datasets.