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We introduce a new method for generating color images from sketches or edge maps. Current methods either require some form of additional user-guidance or are limited to the ``paired’’ translation approach. We argue that segmentation information could provide valuable guidance for sketch colorization. To this end, we propose to leverage semantic image segmentation, as provided by a general purpose panoptic segmentation network, to create an additional adversarial loss function. Our loss function can be integrated to any baseline GAN model.

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Visual light photography, infrared reflectography, ultraviolet fluorescence photography and x-radiography reveal even hidden compositional layers in paintings. To investigate the connections between these images, a multi-modal registration is essential. Due to varying image resolutions, modality dependent image content and depiction styles, registration poses a challenge. Historical paintings usually show crack structures called craquelure in the paint.

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Energy resolved neutron imaging (ERNI) is an advanced neutron radiography technique capable of non-destructively extracting spatial isotopic information within a given material. Energy-dependent radiography image sequences can be created by utilizing neutron time-of-flight techniques. In combination with uniquely characteristic isotopic neutron crosssection spectra, isotopic areal densities can be determined on a per-pixel basis, thus resulting in a set of areal density images for each isotope present in the sample.

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A learning algorithm referred to as Maximum Margin (MM) is proposed for considering the class-imbalance data learning issue: the deep model tends to predict the majority classes rather than the minority ones. For better generalization on the minority classes, the proposed Maximum Margin (MM) loss function is newly designed by minimizing a margin-based generalization bound through the shifting decision bound. As a prior study, the theoretically principled label-distributionaware margin (LDAM) loss had been successfully applied with classical strategies such as re-weighting or re-sampling.

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A singular problem that mars the wide applicability of machine learning (ML) models is the lack of generalizability and interpretability. The ML community is increasingly working on bridging this gap. Prominent among them are methods that study causal significance of features, with techniques such as Average Causal Effect (ACE). In this paper, our objective is to utilize the causal analysis framework to measure the significance level of the features in binary classification task.

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Automatic License Plate Recognition (ALPR) for years has remained a persistent topic of research due to numerous practicable applications, especially in the Intelligent Transportation system (ITS). Many currently available solutions are still not robust in various real-world circumstances and often impose constraints like fixed backgrounds and constant distance and camera angles. This paper presents an efficient multi-language repudiate ALPR system based on machine learning.

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Autonomous Vehicles promise to transport people in a safer, accessible, and even efficient way. Nowadays, real-world autonomous vehicles are build by large teams from big companies with a tremendous amount of engineering effort. Deep Reinforcement Learning can be used instead, without domain experts, to learn end-to-end driving policies. Here, we combine Curriculum Learning with deep reinforcement learning, in order to learn without any prior domain knowledge, an end-to-end competitive driving policy for the CARLA autonomous driving simulator.

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Fine-grained human action recognition is a core research topic in computer vision. Inspired by the recently proposed hierarchy representation of fine-grained actions in FineGym and SlowFast network for action recognition, we propose a novel multi-task network which exploits the FineGym hierarchy representation to achieve effective joint learning and prediction for fine-grained human action recognition.

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