Gallery Items tagged Conference Paper

ICon-MaSTEd paper template
Paper template for ICon-MaSTEd submissions
Serhiy Semerikov

siceel 2021 template
Modelo de artigo para submissão no IX Simpósio de Iniciação Científica da Engenharia Elétrica (IX SICEEL) realizado durante a Semana de integração da Engenharia Elétrica (SIEEL), em parceria com a Universidade Federal de São Carlos (UFSCar) e Universidade de São Paulo (USP)
Marcus Vinicius do Amaral Pires and Luiz Gustavo Soares Martins

Template for SBSR (Brazilian Symposium on Remote Sensing)
Template in English for SBSR (Brazilian Symposium on Remote Sensing)
Rogério Flores Junior, Thales Sehn Körting

ACL 2016 Proceedings Template
This document contains instructions for preparing ACL 2016 submissions and camera-ready manuscripts. The document itself conforms to its own specifications, and is therefore an example of what your manuscript should look like. Papers are required to conform to all the directions reported in this document. By using the provided LaTeX and BibTeX styles (acl2016.sty, acl2016.bst), the required formatting will be enabled by default.
Adi Renduchinala

ICES VI Template
A template for creating ICES conference proceedings submissions.
Donna LaLonde

Template LaTeX JNM 2022
Template LaTeX pour les
22èmes journées Nationales Microondes
7 au 10 juin 2022 - Limoges
Date limite de soumission : 06 décembre 2021
https://jnm2022.sciencesconf.org/
Kevin Nadaud

Template for Submission to IJCAI-19
Template for Submission to IJCAI-19; downloaded from the conference's Author's Kit page.
IJCAI

Our current ISon template
Template for the Interactive Sonification (ISon) workshop proceedings.
Kjetil Falkenberg Hansen

Conservative Wasserstein Training for Pose Estimation
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.
Xiaofeng Liu, Yang Zou, Tong Che, Peng Ding, Ping Jia, Jane You, B.V.K. Vijaya Kumar