Titlebook: Computer Vision – ACCV 2018; 14th Asian Conferenc C. V. Jawahar,Hongdong Li,Konrad Schindler Conference proceedings 2019 Springer Nature Sw

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書目名稱Computer Vision – ACCV 2018影響因子(影響力)




書目名稱Computer Vision – ACCV 2018影響因子(影響力)學(xué)科排名




書目名稱Computer Vision – ACCV 2018網(wǎng)絡(luò)公開度




書目名稱Computer Vision – ACCV 2018網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Computer Vision – ACCV 2018被引頻次




書目名稱Computer Vision – ACCV 2018被引頻次學(xué)科排名




書目名稱Computer Vision – ACCV 2018年度引用




書目名稱Computer Vision – ACCV 2018年度引用學(xué)科排名




書目名稱Computer Vision – ACCV 2018讀者反饋




書目名稱Computer Vision – ACCV 2018讀者反饋學(xué)科排名

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Zero-Shot Facial Expression Recognition with Multi-label Label Propagationmotional classes to describe the varied and nuancing meaning conveyed by facial expression. However, it is almost impossible to enumerate all the emotional categories and collect adequate annotated samples for each category. To this end, we propose a zero-shot learning framework with multi-label lab

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COSONet: Compact Second-Order Network for Video Face Recognitionpose, and also suffer from video-type noises such as motion blur, out-of-focus blur and low resolution. To tackle these two types of challenges, we propose an extensive framework which contains three aspects: neural network design, training data augmentation, and loss function. First, we devise an e

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Understanding Individual Decisions of CNNs via Contrastive Backpropagationr understand individual decisions of deep convolutional neural networks. The saliency maps produced by them are proven to be non-discriminative. Recently, the Layer-wise Relevance Propagation (LRP) approach was proposed to explain the classification decisions of rectifier neural networks. In this wo

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Say Yes to the Dress: Shape and Style Transfer Using Conditional GANsmage, while maintaining the image content and object shapes. In this paper we transfer both the shape and style of chosen objects between images, leaving the remaining areas unaltered. To tackle this problem, we propose a two stage method, where each stage contains a generative adversarial network,

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Towards Multi-class Object Detection in Unconstrained Remote Sensing Imageryfic monitoring and disaster management. The huge variation in object scale, orientation, category, and complex backgrounds, as well as the different camera sensors pose great challenges for current algorithms. In this work, we propose a new method consisting of a novel joint image cascade and featur