Search Results - "\"((\\"our presentation.\\") OR (\\"((our representation) OR (point representation)).\\"))\""

Art and Illusionists by Wade, Nicholas

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City Water Matters Cultures, Practices and Entanglements of Urban Water / by Watson, Sophie

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Data-Driven Wireless Networks A Compressive Spectrum Approach / by Gao, Yue, Qin, Zhijin

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Deep Learning-Based Face Analytics. by Ratha, Nalini K.

Table of Contents: “…Intro -- Contents -- 1 Deep CNN Face Recognition: Looking at the Past and the Future -- 1.1 Synonyms -- 1.2 Introduction -- 1.3 Datasets -- 1.4 Face Detection -- 1.5 Loss Functions -- 1.6 Face Verification and Identification Using CNNs -- 1.7 Open Problems -- References -- 2 Face Segmentation, Face Swapping, and How They Impact Face Recognition -- 2.1 Introduction -- 2.2 Related Work -- 2.2.1 Face Segmentation -- 2.2.2 Face Swapping -- 2.3 Swapping Faces in Unconstrained Images -- 2.3.1 Fitting 3D Face Shapes -- 2.3.2 Deep Face Segmentation -- 2.3.3 Face Swapping and Blending -- 2.4 Experiments -- 2.4.1 Face Segmentation Evaluations -- 2.4.2 Qualitative Face Swapping Results -- 2.4.3 Qualitative Ablation Study -- 2.4.4 Limitations of Our System -- 2.5 The effects of Swapping on Recognition -- 2.5.1 Face Verification System -- 2.5.2 Inter-Subject Swapping Verification Protocols -- 2.5.3 Inter-Subject Swapping Results -- 2.5.4 Intra-Subject Swapping Verification Protocols and Results -- 2.6 Conclusions -- References -- 3 Disentangled Representation Learning and Its Application to Face Analytics -- 3.1 Introduction -- 3.2 Application 1: Facial Landmark Tracking -- 3.2.1 Related Works -- 3.2.2 Our Approach: RED-Net -- 3.2.3 Experiments -- 3.3 Application 2: Learning Facial Representations for Inference and Generation -- 3.3.1 Related Works -- 3.3.2 Our Approach: CR-GAN -- 3.3.3 Results: Multi-view Facial Image Generation -- 3.3.4 Results: Conditional Facial Attribute Manipulation -- 3.4 Conclusion -- References -- 4 Learning 3D Face Morphable Model from In-the-Wild Images -- 4.1 Introduction -- 4.2 Prior Work -- 4.2.1 Linear 3DMM -- 4.2.2 Improving Linear 3DMM -- 4.2.3 2D Face Alignment -- 4.2.4 3D Face Reconstruction -- 4.2.5 Unsupervised Learning in 3DMM -- 4.3 The Proposed Nonlinear 3DMM -- 4.3.1 Conventional Linear 3DMM -- 4.3.2 Nonlinear 3DMM.…”
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The international handbook of black community mental health /

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Quality Software Through Reuse and Integration

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