识别野外家庭(RFIW):与ACM MM 2017联合举办的数据挑战研讨会

Joseph P. Robinson, Ming Shao, Handong Zhao, Yue Wu, Timothy Gillis, Y. Fu
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引用次数: 36

摘要

野外家庭识别(RFIW)是一种大规模、多轨自动亲属识别评估系统,支持比以往更大规模的亲属验证和家庭分类。它是与ACM多媒体2017联合举办的数据挑战研讨会。这是通过支持基于亲属的视觉任务的最大图像集实现的。最后,我们用这份手稿来总结评估协议,取得的进展和一些技术背景和性能评级所使用的算法,并讨论了有希望的方向,为研究和工程师在这方面的下一步工作。
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Recognizing Families In the Wild (RFIW): Data Challenge Workshop in conjunction with ACM MM 2017
Recognizing Families In the Wild (RFIW) is a large-scale, multi-track automatic kinship recognition evaluation, supporting both kinship verification and family classification on scales much larger than ever before. It was organized as a Data Challenge Workshop hosted in conjunction with ACM Multimedia 2017. This was achieved with the largest image collection that supports kin-based vision tasks. In the end, we use this manuscript to summarize evaluation protocols, progress made and some technical background and performance ratings of the algorithms used, and a discussion on promising directions for both research and engineers to be taken next in this line of work.
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Kin-Verification Model on FIW Dataset Using Multi-Set Learning and Local Features RFIW 2017: LPQ-SIEDA for Large Scale Kinship Verification Session details: Keynote & Invited Talks Recent Progress in Deep Reinforcement Learning for Computer Vision and NLP KinNet
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