Titlebook: Computer Vision – ACCV 2020; 15th Asian Conferenc Hiroshi Ishikawa,Cheng-Lin Liu,Jianbo Shi Conference proceedings 2021 Springer Nature Swi

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書目名稱Computer Vision – ACCV 2020影響因子(影響力)




書目名稱Computer Vision – ACCV 2020影響因子(影響力)學(xué)科排名




書目名稱Computer Vision – ACCV 2020網(wǎng)絡(luò)公開度




書目名稱Computer Vision – ACCV 2020網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Computer Vision – ACCV 2020被引頻次




書目名稱Computer Vision – ACCV 2020被引頻次學(xué)科排名




書目名稱Computer Vision – ACCV 2020年度引用




書目名稱Computer Vision – ACCV 2020年度引用學(xué)科排名




書目名稱Computer Vision – ACCV 2020讀者反饋




書目名稱Computer Vision – ACCV 2020讀者反饋學(xué)科排名

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Lecture Notes in Physics Monographsdily available in our daily life, thermal cameras are expensive and less prevalent. It is costly to collect a large quantity of synchronous visible and thermal facial images. To tackle this paired training data bottleneck, we propose an unpaired multimodal facial expression recognition method, which

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https://doi.org/10.1007/3-540-36409-9be classified into coordinate regression methods and heatmap based methods. However, the former loses spatial information, resulting in poor performance while the latter suffers from large output size or high post-processing complexity. This paper proposes a new solution, Gaussian Vector, to preserv

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https://doi.org/10.1007/3-540-36409-9into two classes: top-down and bottom-up methods. Both of the two types of methods involve two stages, namely, person detection and joints detection. Conventionally, the two stages are implemented separately without considering their interactions between them, and this may inevitably cause some issu

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