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Name: 赵鹏
Type: User
Company: @OurEDA_Lab
Bio: I'm ZhaoPeng, I'm fighting.
Location: China-ShenZhen
Blog: www.zhaopengdut.top
Name: 赵鹏
Type: User
Company: @OurEDA_Lab
Bio: I'm ZhaoPeng, I'm fighting.
Location: China-ShenZhen
Blog: www.zhaopengdut.top
BiYiYuanLi
C++
we propose a novel and efficient cross-domain human parsing model to bridge the cross-domain differences in terms of visual appearance and environment conditions and fully exploit commonalities across domains. Our proposed model explicitly learns a feature compensation net-work, which is specialized for mitigating the cross-domain differences. A discriminative feature adversarial network is introduced to supervise the feature compensation to effectively reduces the discrepancy between feature distributions of two domains. Besides, our proposed model also introduces a structured label adversarial network to guide the parsing results of the target domain to follow the high-order relationships of the structured labels shared across domains. The proposed framework is end-to-end trainable, practical and scalable in real applications. Extensive experiments are con- ducted where LIP dataset is the source domain and 4 different datasets including surveillance videos, movies and run- way shows without any annotations, are evaluated as target domains. The results consistently confirm data efficiency and performance advantages of the proposed method for the challenging cross-domain human parsing problem.
Convolutional Neural Networks
Deep convolutional generative adversarial network with SE-ResNet blocks implemented in pytorch
web-based-simulator
无监督学习图像配准
基于环信和Bmob仿腾讯QQ的Demo,目前可以登录注册,加好友功能待测试,后续再陆续更新
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