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20152021
most citedShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design

240 citations

Showing 2018Show all

8 papers · 1 filter

cs.CV2018★ 15 cited

Modeling Local Geometric Structure of 3D Point Clouds using Geo-CNN

Shiyi Lan, Ruichi Yu, Gang Yu +1

Recent advances in deep convolutional neural networks (CNNs) have motivated researchers to adapt CNNs to directly model points in 3D point clouds. Modeling local structure has been…

cs.CV2018★ 41 cited

Bounding Box Regression with Uncertainty for Accurate Object Detection

Yihui He, Chenchen Zhu, Jianren Wang +2

Large-scale object detection datasets (e.g., MS-COCO) try to define the ground truth bounding boxes as clear as possible. However, we observe that ambiguities are still introduced…

cs.CV2018★ 46 cited

Mask TextSpotter: An End-to-End Trainable Neural Network for Spotting Text with Arbitrary Shapes

Pengyuan Lyu, Minghui Liao, Cong Yao +2

Recently, models based on deep neural networks have dominated the fields of scene text detection and recognition. In this paper, we investigate the problem of scene text spotting,…

cs.CV2018★ 240 cited

ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design

Ningning Ma, Xiangyu Zhang, Hai-Tao Zheng +1

Currently, the neural network architecture design is mostly guided by the \emph{indirect} metric of computation complexity, i.e., FLOPs. However, the \emph{direct} metric, e.g., sp…

cs.CV2018★ 32 cited

TextSnake: A Flexible Representation for Detecting Text of Arbitrary Shapes

Shangbang Long, Jiaqiang Ruan, Wenjie Zhang +3

Driven by deep neural networks and large scale datasets, scene text detection methods have progressed substantially over the past years, continuously refreshing the performance rec…

cs.CV2018★ 3 cited

Stroke-based Character Reconstruction

Zhewei Huang, Wen Heng, Yuanzheng Tao +1

Background elimination for noisy character images or character images from real scene is still a challenging problem, due to the bewildering backgrounds, uneven illumination, low r…