ReFace: Improving Clothes-Changing Re-Identification With Face Features

Person re-identification (ReID) has been an active research field for many years. Despite that, models addressing this problem tend to perform poorly when the task is to re-identify the same people over a prolonged time, due to appearance changes such as different clothes and hairstyles. In this work, we introduce a new method that takes full advantage of the ability of existing ReID models to extract appearance-related features and combines it with a face feature extraction model to achieve new state-of-the-art results, both on image-based and video-based benchmarks. Moreover, we show how our method could be used for an application in which multiple people of interest, under clothes-changing settings, should be re-identified given an unseen video and a limited amount of labeled data. We claim that current ReID benchmarks do not represent such real-world scenarios, and publish a new dataset, 42Street, based on a theater play as an example of such an application. We show that our proposed method outperforms existing models also on this dataset while using only pre-trained modules and without any further training

Constraints as Features

In this paper, we introduce a new approach to con-strained clustering which treats the constraints as features. Our method augments the original feature space with ad-ditional dimensions, each of which derived from a given Cannot-link constraints. The specified Cannot-link pair gets extreme coordinates values, and the rest of the points get coordinate values that express their spatial influence from the specified constrained pair. After augmenting all the new features, a standard unconstrained clustering al-gorithm can be performed, like k-means or spectral clus-tering. We demonstrate the efficacy of our method for ac-tive semi-supervised learning applied to image segmenta-tion and compare it to alternative methods. We also eval-uate the performance of our method on the four most com-monly evaluated datasets from the UCI machine learning repository. 1

What distinguishes positive deviance (PD) health professionals from their peers and what impact does a PD intervention have on behaviour change: a cross-sectional study of infection control and prevention in three Israeli hospitals

Past studies using the positive deviance (PD) approach in the field of infection prevention and control (IPC) have primarily focused on impacts on healthcare-associated infection rates. This research aimed to determine if health professionals who exhibit PD behaviours have distinctive socio-cognitive profiles compared to non-PD professionals, and to examine the impact of a PD intervention on healthcare professionals’ (HPs) behavioural changes in maintaining IPC guidelines. In a cross-sectional study among 135 HPs, respondents first filled out a socio-cognitive characteristics questionnaire, and after 5 months were requested to complete a selfreported behavioural change questionnaire. The main findings indicate that socio-cognitive variables such as external locus of control, perceived threat and social learning were significant predictors of a person exhibiting PD behaviours. Almost 70% of HPs reported behavioural change and creating social networks as a result of the PD intervention in maintaining IPC guidelines, 16.9% of them are a ‘PD boosters’ (a new group of HPs who have adopted the positive practices of PDs that were originally identified, and also added additional practices of their own). Social networks can contribute to internalizing and raising personal accountability even among non-PD professionals, by creating a mind map that makes each person believe they are an important node in the network, regardless of their status and role. Health intervention programmes should purposely make visible and prominent social network connections in the hospital system.publishedVersio