Self-training Guided Adversarial Domain Adaptation For Thermal Imagery

Deep models trained on large-scale RGB image datasets have shown tremendous success. It is important to apply such deep models to real-world problems. However, these models suffer from a performance bottleneck under illumination changes. Thermal IR cameras are more robust against such changes, and thus can be very useful for the real-world problems. In order to investigate efficacy of combining feature-rich visible spectrum and thermal image modalities, we propose an unsupervised domain adaptation method which does not require RGB-to-thermal image pairs. We employ large-scale RGB dataset MS-COCO as source domain and thermal dataset FLIR ADAS as target domain to demonstrate results of our method. Although adversarial domain adaptation methods aim to align the distributions of source and target domains, simply aligning the distributions cannot guarantee perfect generalization to the target domain. To this end, we propose a self-training guided adversarial domain adaptation method to promote generalization capabilities of adversarial domain adaptation methods. To perform self-training, pseudo labels are assigned to the samples on the target thermal domain to learn more generalized representations for the target domain. Extensive experimental analyses show that our proposed method achieves better results than the state-of-the-art adversarial domain adaptation methods. The code and models are publicly available.Comment: Accepted to CVPR 2021 Perception Beyond the Visible Spectrum (PBVS) worksho

Perceptions of Parents and Physicians Concerning the Childhood Asthma Control Test

Background. The Childhood Asthma Control Test (C-ACT) has been proposed to be a simple, patient-based test that is able to reflect the multidimensional nature of asthma control. In this analysis, the aim was to evaluate the perceptions of physicians and caregivers concerning C-ACT and its predictive value for future asthma-related events. Method. In a multicenter prospective design, 368 children aged 4-11 years with asthma who were either well-or not well-controlled were included in the study. The study participants were evaluated during three visits made at 2-month intervals and the Turkish version of C-ACT was completed each month. Parents completed questionnaires concerning their perception of asthma (before and after the study) and the C-ACT (after the study). Physicians completed a survey about their perception of a control-based approach and the C-ACT. Results. The C-ACT scores increased from visit 1 to visit 3, with improvement seen in all domains of the test. At the end of the study period, the parents more strongly agreed that asthma could be controlled completely and that asthma attacks and nocturnal awakenings due to asthma were preventable (p < .05). Most of the parents reported that the C-ACT helped them to determine asthma treatment goals for their children and also that the C-ACT improved communication with their physicians. The physicians indicated that a control-centered approach was more convenient (95%) and simpler (94.5%) thana severity-centered approach and provided better disease control (93.4%). A higher C-ACT score was associated with a decreased risk of asthma attack and emergency department admittance in the 2 months following the administration of C-ACT. Conclusion. Our findings indicated that the C-ACT improved both parental outlook on asthma control and the communication between the physician and parents. There was a good correlation between the C-ACT score and the level of asthma control achieved, as described by the physician. Additionally the C-ACT score was predictive of future asthma-related events. These findings suggest that the C-ACT may have an important role in asthma management in the future