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

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书目名称Computer Vision – ACCV 2018影响因子(影响力)




书目名称Computer Vision – ACCV 2018影响因子(影响力)学科排名




书目名称Computer Vision – ACCV 2018网络公开度




书目名称Computer Vision – ACCV 2018网络公开度学科排名




书目名称Computer Vision – ACCV 2018被引频次




书目名称Computer Vision – ACCV 2018被引频次学科排名




书目名称Computer Vision – ACCV 2018年度引用




书目名称Computer Vision – ACCV 2018年度引用学科排名




书目名称Computer Vision – ACCV 2018读者反馈




书目名称Computer Vision – ACCV 2018读者反馈学科排名

Bridle

Dealing with Ambiguity in Robotic Grasping via Multiple Predictionsel that predicts a set of grasp hypotheses in under 60 ms, which is critical for real-time robotic applications. The grasp detection accuracy reaches over . for unseen objects, outperforming the current state of the art on this task.

滔滔不绝地讲

勋章

Robust Deep Multi-modal Learning Based on Gated Information Fusion Networkmodality according to the input feature maps to be fused. The combining weights are determined by applying the convolutional layers followed by the sigmoid function to the concatenated intermediate feature maps. The whole network including the CNN backbone and GIF is trained in an end-to-end fashion

ironic

Hardware-Aware Softmax Approximation for Deep Neural Networksating cost-intensive operations in Softmax (. exponential and division) with cost-effective operations (. addition and bit shifts). We designed and synthesized a hardware unit for our approximation approach, to estimate the area and energy consumption. In addition, we introduce a training method to

远地点

Video Object Segmentation with Language Referring Expressions. We show that our language-supervised approach performs on par with the methods which have access to a pixel-level mask of the target object on . and is competitive to methods using scribbles on the challenging . dataset.

远地点

Nonlinear Subspace Feature Enhancement for Image Set Classificatione of subspace-based classifiers such as sparse representation-based classification. We describe how the structured loss function of NSFE can be optimized in a batch-by-batch fashion by a two-step alternating algorithm. The algorithm makes very few assumptions about the form of the embedding to be le

ingrate

Adversarial Learning for Visual Storytelling with Sense Group Partitionup as the unit, we propose to do the paragraph generation at sense group level instead of sentence level. Experiments on the widely-used dataset show that our approach generates higher-quality descriptions than previous baselines.

Conflagration

Hemiplegia