Recognizing Families In the Wild (RFIW): The 4th Edition

被引:0
|
作者
Robinson, Joseph P. [1 ]
Yin, Yu [1 ]
Khan, Zaid [1 ]
Shao, Ming [2 ]
Xia, Siyu [3 ]
Stopa, Michael [4 ]
Timoner, Samson [5 ]
Turk, Matthew A. [6 ]
Chellappa, Rama [7 ]
Fu, Yun [1 ]
机构
[1] Northwestern Univ, Evanston, IL 60208 USA
[2] UMass Dartmouth, N Dartmouth, MA USA
[3] Southeast Univ, Nanjing, Peoples R China
[4] Konica Minolta, Tokyo, Tokyo, Japan
[5] ISM Connect, Doylestown, PA USA
[6] Toyota Technol Inst Chicago TTIC, Chicago, IL USA
[7] Univ Maryland, College Pk, MD 20742 USA
关键词
D O I
10.1109/fg47880.2020.00138
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
YYYY Recognizing Families In the Wild (RFIW)- an annual large-scale, multi-track automatic kinship recognition evaluation- supports various visual kin-based problems on scales much higher than ever before. Organized in conjunction with the as a Challenge, RFIW provides a platform for publishing original work and the gathering of experts for a discussion of the next steps. This paper summarizes the supported tasks (i.e., kinship verification, tri-subject verification, and search & retrieval of missing children) in the evaluation protocols, which include the practical motivation, technical background, data splits, metrics, and benchmark results. Furthermore, top submissions (i.e., leader-board stats) are listed and reviewed as a high-level analysis on the state of the problem. In the end, the purpose of this paper is to describe the 2020 RFIW challenge, end-to-end, along with forecasts in promising future directions.
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页码:857 / 862
页数:6
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