Titlebook: Computer Vision – ECCV 2016; 14th European Confer Bastian Leibe,Jiri Matas,Max Welling Conference proceedings 2016 Springer International P

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Towards Viewpoint Invariant 3D Human Pose Estimationy collected human pose dataset containing 100 K annotated depth images from extreme viewpoints. Experiments show that our model achieves competitive performance on frontal views while achieving state-of-the-art performance on alternate viewpoints.

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Deep Learning the City: Quantifying Urban Perception at a Global Scales data, we train a Siamese-like convolutional neural architecture, which learns from a joint classification and ranking loss, to predict human judgments of pairwise image comparisons. Our results show that crowdsourcing combined with neural networks can produce urban perception data at the global scale.

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Learnable Histogram: Statistical Context Features for Deep Neural Networksct detection, are explored by integrating the learnable histogram layer into deep networks, which show that the proposed layer could be well generalized to different applications. In-depth investigations are conducted to provide insights on the newly introduced layer.

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0302-9743 recognition and retrieval; scene understanding; optimization; image and video processing; learning; action, activity and tracking; 3D; and 9 poster sessions..978-3-319-46447-3978-3-319-46448-0Series ISSN 0302-9743 Series E-ISSN 1611-3349

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Leslie M. Beebe,Tomoko Takahashin component. Experimental results on the PASCAL VOC, COCO, and ILSVRC datasets confirm that SSD has competitive accuracy to methods that utilize an additional object proposal step and is much faster, while providing a unified framework for both training and inference. For . input, SSD achieves 74.3 

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https://doi.org/10.1007/978-1-4899-0900-8ion LSTM (A-LSTM) and refinement LSTM (R-LSTM) models are introduced in RAR. At each recurrent stage, A-LSTM implicitly identifies a reliable landmark as the attention center. Following the sequence of attention centers, R-LSTM sequentially refines the landmarks near or correlated with the attention

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