[visionlist] IEEE TPAMI Special Issue: Inpainting and Denoising, call for papers
sergio.escalera.guerrero at gmail.com
Tue Aug 14 13:03:31 -05 2018
*IEEE TPAMI Special Issue: Inpainting and Denoising, call for papers*
Contact: sergio.escalera.guerrero at gmail.com
*Aims and scope**: * The problem of dealing with missing data or incomplete
data in machine learning arises in many applications. Recent strategies
make use of generative models to impute missing or corrupted data. Advances
in computer vision using deep generative models have found applications in
image/video processing, such as denoising , restoration ,
super-resolution , or inpainting [4,5]. We focus on image and video
inpainting tasks, that might benefit from novel methods such as Generative
Adversarial Networks (GANs) [6,7] or Residual connections [8,9]. Solutions
to the inpainting problem may be useful in a wide variety of computer
*Article contribution**: *The scope comprises all aspects of image and
video inpainting and denoising. Including but not limited to the following
- 2D/3D human pose recovery under occlusion,
- human inpainting,
- human retexturing,
- video decaptioning,
- temporal occlusion recovery,
- object recognition under occlusion,
- fingerprint recognition,
- fingerprint denoising,
- future frame video prediction,
- unsupervised learning for missing data recovery and/or denoising,
- new data and applications of inpainting and/or denoising.
Article submission instructions:
 V. Jain and S. Seung, “Natural image denoising with convolutional
networks,” in Advances in Neural Information Processing Systems, 2009, pp.
 L. Xu, J. S. Ren, C. Liu, and J. Jia, “Deep convolutional neural
network for image deconvolution,” in Advances in Neural Information
Processing Systems 27, Z. Ghahramani, M. Welling, C. Cortes, N.
D. Lawrence, and K. Q. Weinberger, Eds. Curran Associates, Inc., 2014,
 C. Dong, C. C. Loy, K. He, and X. Tang, “Image super-resolution using
deep convolutional networks,” IEEE transactions on pattern analysis and
machine intelligence, vol. 38, no. 2, pp. 295–307, 2016.
 J. Xie, L. Xu, and E. Chen, “Image denoising and inpainting with deep
neural networks,” in Advances in Neural Information Processing Systems,
2012, pp. 341–349.
 A. Newson, A. Almansa, M. Fradet, Y. Gousseau, and P. P´erez, “Video
inpainting of complex scenes,” SIAM Journal on Imaging Sciences, vol. 7,
no. 4, pp. 1993–2019, 2014.
 I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S.
Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” in
Advances in neural information processing systems, 2014, pp. 2672–2680.
 D. Pathak, P. Kr¨ahenb¨uhl, J. Donahue, T. Darrell, and A. Efros,
“Context encoders: Feature learning by inpainting,” in Computer Vision and
Pattern Recognition (CVPR), 2016.
 K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image
recognition,” in The IEEE Conference on Computer Vision and Pattern
Recognition (CVPR), June 2016.
 X.-J. Mao, C. Shen, and Y.-B. Yang, “Image Restoration Using
Convolutional Auto-encoders with Symmetric Skip Connections,” ArXiv
e-prints, Jun. 2016.
*Dr. Sergio Escalera Guerrero*Head of Human Pose Recovery and Behavior
Project Manager at the Computer Vision Center
Director of ChaLearn Challenges in Machine Learning
Associate professor at University of Barcelona / Universitat Oberta de
Catalunya / Aalborg Univ. /
Email: sergio.escalera.guerrero at gmail.com / Webpage:
-------------- next part --------------
An HTML attachment was scrubbed...
More information about the visionlist