Titlebook: Computer Vision –ACCV 2016; 13th Asian Conferenc Shang-Hong Lai,Vincent Lepetit,Yoichi Sato Conference proceedings 2017 Springer Internatio

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書(shū)目名稱(chēng)Computer Vision –ACCV 2016影響因子(影響力)




書(shū)目名稱(chēng)Computer Vision –ACCV 2016影響因子(影響力)學(xué)科排名




書(shū)目名稱(chēng)Computer Vision –ACCV 2016網(wǎng)絡(luò)公開(kāi)度




書(shū)目名稱(chēng)Computer Vision –ACCV 2016網(wǎng)絡(luò)公開(kāi)度學(xué)科排名




書(shū)目名稱(chēng)Computer Vision –ACCV 2016被引頻次




書(shū)目名稱(chēng)Computer Vision –ACCV 2016被引頻次學(xué)科排名




書(shū)目名稱(chēng)Computer Vision –ACCV 2016年度引用




書(shū)目名稱(chēng)Computer Vision –ACCV 2016年度引用學(xué)科排名




書(shū)目名稱(chēng)Computer Vision –ACCV 2016讀者反饋




書(shū)目名稱(chēng)Computer Vision –ACCV 2016讀者反饋學(xué)科排名

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Visual Saliency Detection for RGB-D Images with Generative Modelmodel. The depth feature map is extracted based on superpixel contrast computation with spatial priors. We model the depth saliency map by approximating the density of depth-based contrast features using a Gaussian distribution. Similar to the depth saliency computation, the colour saliency map is c

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Generalized Fusion Moves for Continuous Label Optimizationpixel lattices and seek to assign discrete or continuous values (or both) to each pixel such that a combined data term and a spatial smoothness prior are minimized. In this work we propose to minimize difficult energies using repeated generalized fusion moves. In contrast to standard fusion moves, t

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phi-LSTM: A Phrase-Based Hierarchical LSTM Model for Image Captioningbe their attributes, and recognize their relationships/interactions. In this paper, we propose a phrase-based hierarchical Long Short-Term Memory (phi-LSTM) model to generate image description. The proposed model encodes sentence as a sequence of combination of phrases and words, instead of a sequen

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Using Gaussian Processes to Improve Zero-Shot Learning with Relative Attributesimage is expressed in terms of attributes that are relatively specified between different class pairs. However, for zero-shot learning the authors had assumed a simple Gaussian Mixture Model (GMM) that used the GMM based clustering to obtain the label for an unknown target test example. In this pape