No abstract

Image relighting is attracting increasing interest due to its various applications. From a research perspective, image relighting can be exploited to conduct both image normalization for domain adaptation, and also for data augmentation. It also has multiple direct uses for photo montage and aesthetic enhancement. In this paper, we review the NTIRE 2021 depth guided image relighting challenge.We rely on the VIDIT dataset for each of our two challenge tracks, including depth information. The first track is on one-to-one relighting where the goal is to transform the illumination setup of an input image (color temperature and light source position) to the target illumination setup. In the second track, the any-to-any relighting challenge, the objective is to transform the illumination settings of the input image to match those of another guide image, similar to style transfer. In both tracks, participants were given depth information about the captured scenes. We had nearly 250 registered participants, leading to 18 confirmed team submissions in the final competition stage. The competitions, methods, and final results are presented in this paper.

Purpose The purpose of this paper is to conduct an exploratory analysis of the demographic factors and investors’ characteristics, which cause changes in the extent of overconfidence level and its constituents among the individuals. Design/methodology/approach A survey has been conducted to explore the determinants of overconfidence and its constituents with the help of a well-structured close-ended questionnaire. The four constituents of overconfidence considered for the study are “better than average effect,” “planning fallacy,” “self-attribution” and “positive illusion.” The collected data are analyzed with the help of t-test, ANOVA and standard ordinary least square regression. Findings The results show that those who earn high, have more dependents, share the earning responsibility, have high investment frequency, less time horizon and more investment experience and invest in large cap stocks are more subject to the overconfidence. The study also concludes that gender, age and general education do not affect the level of overconfidence. Research limitations/implications The results of the study are useful for the market regulators, financial educators, stock market advisors and individual investors in avoiding costly investment mistakes, especially when transiting from one category of demographic and investment characteristics to another category of demographic and investment characteristics. Originality/value The study is unique in itself, as it contributes an instrument to quantify the level of overconfidence among the individual investors. Moreover, the study attempts to explore the impact of all demographic and investment characteristics in one go, which makes it a valuable contribution in the existing literature.

This paper summarizes the top contributions to the first challenge on thermal image super-resolution (TISR), which was organized as part of the Perception Beyond the Visible Spectrum (PBVS) 2020 workshop. In this challenge, a novel thermal image dataset is considered together with stateof-the-art approaches evaluated under a common framework. The dataset used in the challenge consists of 1021 thermal images, obtained from three distinct thermal cameras at different resolutions (low-resolution, mid-resolution, and high-resolution), resulting in a total of 3063 thermal images. From each resolution, 951 images are used for training and 50 for testing while the 20 remaining images are used for two proposed evaluations. The first evaluation consists of downsampling the low-resolution, midresolution, and high-resolution thermal images by ×2, ×3 and ×4 respectively, and comparing their super-resolution results with the corresponding ground truth images. The second evaluation is comprised of obtaining the ×2 superresolution from a given mid-resolution thermal image and comparing it with the corresponding semi-registered highresolution thermal image. Out of 51 registered participants, 6 teams reached the final validation phase.

This paper summarizes the top contributions to the first challenge on thermal image super-resolution (TISR), which was organized as part of the Perception Beyond the Visible Spectrum (PBVS) 2020 workshop. In this challenge, a novel thermal image dataset is considered together with stateof-the-art approaches evaluated under a common framework. The dataset used in the challenge consists of 1021 thermal images, obtained from three distinct thermal cameras at different resolutions (low-resolution, mid-resolution, and high-resolution), resulting in a total of 3063 thermal images. From each resolution, 951 images are used for training and 50 for testing while the 20 remaining images are used for two proposed evaluations. The first evaluation consists of downsampling the low-resolution, midresolution, and high-resolution thermal images by ×2, ×3 and ×4 respectively, and comparing their super-resolution results with the corresponding ground truth images. The second evaluation is comprised of obtaining the ×2 superresolution from a given mid-resolution thermal image and comparing it with the corresponding semi-registered highresolution thermal image. Out of 51 registered participants, 6 teams reached the final validation phase.