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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Kooperationsprozesse und Echtzeitmanagementding in only one stage by adopting feature sharing and multi-task learning strategy. Experiments on several benchmarks demonstrate that the proposed method surpasses the previous state-of-the-art segmentation-free methods. (The code is available at ..)

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https://doi.org/10.1007/978-3-642-17032-4ing strategy for labeling in-the-wild facial expressions. Then, RAF-AU was finely annotated by experienced coders, on which we also conducted a preliminary investigation of which key AUs contribute most to a perceived emotion, and the relationship between AUs and facial expressions. Finally, we prov

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https://doi.org/10.1007/978-3-031-24563-3man body model, and transfer to the shape and pose of the target person. Our cloth reconstruction method can be easily applied to diverse cloth categories. Our method produces final try-on output with naturally deformed clothing and preserving details in high resolution.

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The Digital Future of Hospitalitynt variations. Motivated by these, our SXMNet is boosted by bottom-up attention and neural-guided Meta Fusion. Raw input image is exploited to generate high-quality attention masks in a bottom-up way for pyramid feature refinement. Subsequently, the per-stage predictions according to the refined fea

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,The First Division—Security Wing,ed on previous state-of-the-art techniques, modified for the proposed task (17% better Frechet Inception distance and 18% better style classification score). Moreover, the lightweight design of the proposed modules enables the high-quality synthesis at . resolution.