Generating dataset for object recognition from virtual world

Recent success in object recognition has been driven by deep convolution neural networks trained on large datasets. However, the construction of large datasets was extremely costly due to large amount of manpower required, and it is impossible to obtain real images to construct large datasets in some scenes. In this paper we present two approaches to generating synthetic images instead of real images to build datasets for object recognition, which requires little manpower in labeling and generating images. What is more, we studied the impact of factors in the dataset construction process on the quality of the dataset and compared two ways of generating datasets with testing in the real world. Our experiments evidence that the methods of constructing synthetic datasets can solve the object recognition problem to a certain extent, and we give the appropriate factors settings in the process of building synthetic datasets.

[1]  Kate Saenko,et al.  From Virtual to Reality: Fast Adaptation of Virtual Object Detectors to Real Domains , 2014, BMVC.

[2]  Qiang Chen,et al.  Network In Network , 2013, ICLR.

[3]  Jian Sun,et al.  Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition , 2015, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Fei-Fei Li,et al.  ImageNet: A large-scale hierarchical image database , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[5]  Vladlen Koltun,et al.  Playing for Data: Ground Truth from Computer Games , 2016, ECCV.

[6]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[7]  Trevor Darrell,et al.  Adapting Visual Category Models to New Domains , 2010, ECCV.

[8]  Alexei A. Efros,et al.  Unbiased look at dataset bias , 2011, CVPR 2011.

[9]  Yida Wang,et al.  ZigzagNet: Efficient Deep Learning for Real Object Recognition Based on 3D Models , 2016, ACCV.

[10]  Yann LeCun,et al.  The mnist database of handwritten digits , 2005 .

[11]  Cordelia Schmid,et al.  Viewpoint-independent object class detection using 3D Feature Maps , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[12]  Kilian Q. Weinberger,et al.  Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[13]  Jian Sun,et al.  Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Yunde Jia.  Description and recognition of curved objects , 1992, Proceedings., 11th IAPR International Conference on Pattern Recognition. Vol. III. Conference C: Image, Speech and Signal Analysis,.

[15]  Leonidas J. Guibas,et al.  Render for CNN: Viewpoint Estimation in Images Using CNNs Trained with Rendered 3D Model Views , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[16]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[17]  Michael Goesele,et al.  Back to the Future: Learning Shape Models from 3D CAD Data , 2010, BMVC.

[18]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[19]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[20]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[21]  Alex Krizhevsky,et al.  Learning Multiple Layers of Features from Tiny Images , 2009 .

[22]  Kate Saenko,et al.  Learning Deep Object Detectors from 3D Models , 2014, 2015 IEEE International Conference on Computer Vision (ICCV).

[23]  Kate Saenko,et al.  Syn2Real: A New Benchmark forSynthetic-to-Real Visual Domain Adaptation , 2018, ArXiv.

[24]  Roland Vollgraf,et al.  Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms , 2017, ArXiv.