It is our pleasure to invite you to submit extended abstracts (4 pages long
excluding references, with optional appendix) for oral and poster
presentations at The First Workshop on Statistical Deep Learning in
Computer Vision (SDL-CV) which will be held in conjunction with ICCV 2019.
We will also invite selected papers for submission to a special issue on
Statistical Deep Learning for Computer Vision in the International Journal
of Computer Vision (IJCV). Extended versions of selected papers will be
invited for book chapter publication.
Submission Deadline: July 31, 2019
Workshop Website: http://www.sdlcv-workshop.com/
Please find the full CfP below.
Kind regards,
Mete Ozay on behalf of the organizers
=== Workshop Description ===
Deep learning has been a useful and primary toolbox to perform various
computer vision tasks successfully in the recent years. Various seminal
works have been proposed to explain the underlying theory and mechanisms of
these successful algorithms, in order to further improve their various
properties, such as generalization capacity of models, representation
capacity of learned features, convergence and computational complexity of
training methods.
In this workshop, we consider statistical approaches employed to improve
our understanding of deep learning, and to develop methods to boost their
properties, with applications in computer vision, such as object
recognition, detection, segmentation, tracking, scene description, visual
question answering, robot vision, image enhancement and recovery. The
workshop will consist of invited talks, oral talks, poster sessions and a
research panel. Our target audience is graduate students, researchers and
practitioners who have been working on development of novel statistical
deep learning algorithms and/or their application to solve practical
problems in computer vision. Accepted papers will present their results in
the workshop in oral talks and poster sessions. We will also invite
selected papers for submission to a special issue on Statistical Deep
Learning for Computer Vision in the International Journal of Computer
Vision (IJCV). Extended versions of selected papers will be invited for
book chapter publication.
=== Covered Topics ===
We solicit original contributions that deploy statistical deep learning
methods employed to perform various computer vision tasks including, but
not limited to:
- Statistical Understanding of Deep Learning
-- Interpretable deep learning, quantitative measures and analyses
- Statistical Normalization Methods
-- Feature, weight, gradient and hybrid normalization methods
- Uncertainty in Deep Learning
-- Uncertainty measures, adversarial methods, intrinsic and extrinsic
uncertainty of models
- Information Theory of Deep Learning
-- Information geometry, information bottleneck, rate distortion, etc.
- Probabilistic Deep Learning
-- Variational methods, graphical methods, Bayesian learning and inference
-- Bayesian deep learning
-- Neural network architecture search via probabilistic models
- Stochastic Optimization for Deep Learning
-- Optimization on Riemannian manifolds, topological manifolds, and
product manifolds
- Probabilistic Programming for Deep Learning
-- Scene perception, logical reasoning, autonomous driving
- Statistical Meta-learning Algorithms
-- Few-shot learning/incremental learning for image classification and
beyond
-- Zero-shot learning for high-level vision tasks
- Reinforcement Learning for Vision Systems
-- RL algorithms and vision problems
- Causal Deep Learning
-- Causal inference, causal feature learning
=== Call for Papers ===
We invite submissions describing works in the domains suggested above or in
closely-related areas. We encourage the submission of previously published
material (clearly marked as such) that is closely related to the workshop
topic. We will invite the best original papers for an oral plenary
presentation. Accepted papers will be presented in oral/poster sessions at
the workshop and appear in the CVF open access archive. The review process
is single-blind. Each paper will receive strong accept (for oral
candidate), accept or reject decision. Note that there is no author
feedback phase during submission. We will also invite selected papers for
submission to a special issue on Statistical Deep Learning for Computer
Vision in the International Journal of Computer Vision (IJCV). Extended
versions of selected papers will be invited for book chapter publication.
Paper submission deadline: July 31, 2019
Author notification: Sep 4, 2019
Camera-ready deadline: Sep 25, 2019
=== Submission Instructions ===
Format and paper length:
A paper submission has to be in English, in pdf format, and at most FOUR
pages (excluding references). The paper format must follow the same
guidelines as used in the ICCV 2019 submissions. For further details,
please see:
http://www.sdlcv-workshop.com/callforpaper.html
=== Invited Speakers ===
We are proud to have a group of diverse invited speakers covering the
entire spectrum of scene and and situation understanding research:
* Xianfeng Gu, Stony Brook University
* Alex Kendall, University of Cambridge
* Yi Ma, University of California, Berkeley
* Yingnian Wu, University of California, Los Angeles
* Alan L. Yuille, Johns Hopkins University
* Lizhong Zheng, Massachusetts Institute of Technology
=== Organizers ===
Ping Luo, HKU
Mete Ozay, PKSHA
Hongyang Li, CUHK
Chaochao Lu, Cambridge University
Lei Huang, IIAI
Wenqi Shao, CUHK
Xianfeng Gu, Stony Brook University
Alan L. Yuille, Johns Hopkins University
Xiaogang Wang, CUHK
Yi Ma, University of California, Berkeley
Lizhong Zheng, MIT
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