Gallery — Two-column

tccv (two columns curriculum vitae) is a LaTeX class inspired by the template found at latextemplates.com by Alessandro Plasmati. Create by Nicola Fontana, the original files can be downloaded from: http://dev.entidi.com/p/tccv/

This is a template for the preparation of manuscripts to be submitted to the annual International Symposium on Automation and Robotics in Construction (ISARC). This template was last updated on 22/09/2020

For CSEC-427 (Authentication & Security Models) course paper/project at Rochester Institute of Technology

Survey article template for the final exam at the Master's in Security and Applied Logic course "Operating Systems: Design and Security" taught at the University of Bucharest.

généralités sur les fonctions exercices

This document contains the instructions for preparing a manuscript for the proceedings of EMNLP 2020. The document itself conforms to its own specifications, and is therefore an example of what your manuscript should look like. These instructions should be used for both papers submitted for review and for final versions of accepted papers. Authors are asked to conform to all the directions reported in this document. Note from Overleaf: SyncTeX will not work correctly with this template (as well as other templates based on similar underlying code, eg CVPR, EMNLP, etc) when the line numbers are active. To make SyncTeX function while authoring your manuscript, either on Overleaf or in your own LaTeX installation, the line numbers have to be turned off by uncommenting \aclfinalcopy.

LaTeX template for 2019 ACM/IEEE Symposium on Architectures for Networking and Communications Systems (ANCS).

An example of a geometry two-column proof template.

Paper presented at ICCV 2019. This paper targets the task with discrete and periodic class labels (e.g., pose/orientation estimation) in the context of deep learning. The commonly used cross-entropy or regression loss is not well matched to this problem as they ignore the periodic nature of the labels and the class similarity, or assume labels are continuous value. We propose to incorporate inter-class correlations in a Wasserstein training framework by pre-defining (i.e., using arc length of a circle) or adaptively learning the ground metric. We extend the ground metric as a linear, convex or concave increasing function w.r.t. arc length from an optimization perspective. We also propose to construct the conservative target labels which model the inlier and outlier noises using a wrapped unimodal-uniform mixture distribution. Unlike the one-hot setting, the conservative label makes the computation of Wasserstein distance more challenging. We systematically conclude the practical closed-form solution of Wasserstein distance for pose data with either one-hot or conservative target label. We evaluate our method on head, body, vehicle and 3D object pose benchmarks with exhaustive ablation studies. The Wasserstein loss obtaining superior performance over the current methods, especially using convex mapping function for ground metric, conservative label, and closed-form solution.