<div dir="ltr"><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt" id="gmail-docs-internal-guid-708b55fd-7fff-c2c1-21e9-b00eef0e0bcc"><span style="font-size:12pt;font-family:Arial;color:rgb(34,34,34);background-color:rgb(255,255,255);font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">Dear Colleagues,</span></p><br><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:Arial;color:rgb(34,34,34);background-color:rgb(255,255,255);font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">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.</span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:Arial;color:rgb(34,34,34);background-color:rgb(255,255,255);font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap"> </span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:Arial;color:rgb(34,34,34);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">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.</span></p><br><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:Arial;color:rgb(34,34,34);background-color:rgb(255,255,255);font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">Submission Deadline: July 31, 2019</span></p><br><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:Arial;color:rgb(34,34,34);background-color:rgb(255,255,255);font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">Workshop Website:</span><span style="font-size:14pt;font-family:Arial;color:rgb(34,34,34);background-color:rgb(255,255,255);font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap"> </span><a href="http://www.sdlcv-workshop.com/" style="text-decoration:none"><span style="font-size:12pt;font-family:Arial;color:rgb(17,85,204);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:underline;vertical-align:baseline;white-space:pre-wrap">http://www.sdlcv-workshop.com/</span></a></p><br><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:Arial;color:rgb(34,34,34);background-color:rgb(255,255,255);font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">Please find the full CfP below.</span></p><br><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:Arial;color:rgb(34,34,34);background-color:rgb(255,255,255);font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">Kind regards,</span></p><br><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:Arial;color:rgb(34,34,34);background-color:rgb(255,255,255);font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">Mete Ozay on behalf of the organizers</span></p><br><br><br><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:Arial;color:rgb(34,34,34);background-color:rgb(255,255,255);font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">=== Workshop Description ===</span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:Arial;color:rgb(34,34,34);background-color:rgb(255,255,255);font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap"> </span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:Arial;color:rgb(34,34,34);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">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.</span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:Arial;color:rgb(34,34,34);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap"> </span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:Arial;color:rgb(34,34,34);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">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.</span></p><br><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:Arial;color:rgb(34,34,34);background-color:rgb(255,255,255);font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">=== Covered Topics ===</span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:Arial;color:rgb(34,34,34);background-color:rgb(255,255,255);font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap"> </span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:Arial;color:rgb(34,34,34);background-color:rgb(255,255,255);font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">We solicit original contributions that deploy statistical deep learning methods employed to perform various computer vision tasks including, but not limited to:</span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:Arial;color:rgb(34,34,34);background-color:rgb(255,255,255);font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap"> </span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:Arial;color:rgb(34,34,34);background-color:rgb(255,255,255);font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">- Statistical Understanding of Deep Learning</span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:Arial;color:rgb(34,34,34);background-color:rgb(255,255,255);font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap"> </span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:Arial;color:rgb(34,34,34);background-color:rgb(255,255,255);font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">   -- Interpretable deep learning, quantitative measures and analyses</span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:Arial;color:rgb(34,34,34);background-color:rgb(255,255,255);font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap"> </span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:Arial;color:rgb(34,34,34);background-color:rgb(255,255,255);font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">- Statistical Normalization Methods</span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:Arial;color:rgb(34,34,34);background-color:rgb(255,255,255);font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap"> </span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:Arial;color:rgb(34,34,34);background-color:rgb(255,255,255);font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">  -- Feature, weight, gradient and hybrid normalization methods</span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:Arial;color:rgb(34,34,34);background-color:rgb(255,255,255);font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap"> </span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:Arial;color:rgb(34,34,34);background-color:rgb(255,255,255);font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">- Uncertainty in Deep Learning</span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:Arial;color:rgb(34,34,34);background-color:rgb(255,255,255);font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap"> </span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:Arial;color:rgb(34,34,34);background-color:rgb(255,255,255);font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">  -- Uncertainty measures, adversarial methods, intrinsic and extrinsic uncertainty of models</span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:Arial;color:rgb(34,34,34);background-color:rgb(255,255,255);font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap"> </span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:Arial;color:rgb(34,34,34);background-color:rgb(255,255,255);font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">- Information Theory of Deep Learning</span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:Arial;color:rgb(34,34,34);background-color:rgb(255,255,255);font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap"> </span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:Arial;color:rgb(34,34,34);background-color:rgb(255,255,255);font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">   -- Information geometry, information bottleneck, rate distortion, etc.</span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:Arial;color:rgb(34,34,34);background-color:rgb(255,255,255);font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap"> </span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:Arial;color:rgb(34,34,34);background-color:rgb(255,255,255);font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">- Probabilistic Deep Learning</span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:Arial;color:rgb(34,34,34);background-color:rgb(255,255,255);font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap"> </span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:Arial;color:rgb(34,34,34);background-color:rgb(255,255,255);font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">  -- Variational methods, graphical methods, Bayesian learning and inference</span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:Arial;color:rgb(34,34,34);background-color:rgb(255,255,255);font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap"> </span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:Arial;color:rgb(34,34,34);background-color:rgb(255,255,255);font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">  -- Bayesian deep learning</span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:Arial;color:rgb(34,34,34);background-color:rgb(255,255,255);font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap"> </span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:Arial;color:rgb(34,34,34);background-color:rgb(255,255,255);font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">  -- Neural network architecture search via probabilistic models</span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:Arial;color:rgb(34,34,34);background-color:rgb(255,255,255);font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap"> </span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:Arial;color:rgb(34,34,34);background-color:rgb(255,255,255);font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">- Stochastic Optimization for Deep Learning</span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:Arial;color:rgb(34,34,34);background-color:rgb(255,255,255);font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap"> </span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:Arial;color:rgb(34,34,34);background-color:rgb(255,255,255);font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">  -- Optimization on Riemannian manifolds, topological manifolds, and product manifolds</span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:Arial;color:rgb(34,34,34);background-color:rgb(255,255,255);font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap"> </span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:Arial;color:rgb(34,34,34);background-color:rgb(255,255,255);font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">- Probabilistic Programming for Deep Learning</span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:Arial;color:rgb(34,34,34);background-color:rgb(255,255,255);font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap"> </span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:Arial;color:rgb(34,34,34);background-color:rgb(255,255,255);font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">  -- Scene perception, logical reasoning, autonomous driving</span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:Arial;color:rgb(34,34,34);background-color:rgb(255,255,255);font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap"> </span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:Arial;color:rgb(34,34,34);background-color:rgb(255,255,255);font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">- Statistical Meta-learning Algorithms</span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:Arial;color:rgb(34,34,34);background-color:rgb(255,255,255);font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap"> </span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:Arial;color:rgb(34,34,34);background-color:rgb(255,255,255);font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">  -- Few-shot learning/incremental learning for image classification and beyond</span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:Arial;color:rgb(34,34,34);background-color:rgb(255,255,255);font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap"> </span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:Arial;color:rgb(34,34,34);background-color:rgb(255,255,255);font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">  -- Zero-shot learning for high-level vision tasks</span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:Arial;color:rgb(34,34,34);background-color:rgb(255,255,255);font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap"> </span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:Arial;color:rgb(34,34,34);background-color:rgb(255,255,255);font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">- Reinforcement Learning for Vision Systems</span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:Arial;color:rgb(34,34,34);background-color:rgb(255,255,255);font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap"> </span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:Arial;color:rgb(34,34,34);background-color:rgb(255,255,255);font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">  -- RL algorithms and vision problems</span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:Arial;color:rgb(34,34,34);background-color:rgb(255,255,255);font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap"> </span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:Arial;color:rgb(34,34,34);background-color:rgb(255,255,255);font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">- Causal Deep Learning</span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:Arial;color:rgb(34,34,34);background-color:rgb(255,255,255);font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap"> </span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:Arial;color:rgb(34,34,34);background-color:rgb(255,255,255);font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">  -- Causal inference, causal feature learning</span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:Arial;color:rgb(34,34,34);background-color:rgb(255,255,255);font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap"> </span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:Arial;color:rgb(34,34,34);background-color:rgb(255,255,255);font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">=== Call for Papers ===</span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:Arial;color:rgb(34,34,34);background-color:rgb(255,255,255);font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap"> </span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:Arial;color:rgb(34,34,34);background-color:rgb(255,255,255);font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">We invite submissions describing works in the domains suggested above or in closely-related areas. </span><span style="font-size:12pt;font-family:Arial;color:rgb(33,33,33);background-color:rgb(255,255,255);font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">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. </span><span style="font-size:12pt;font-family:Arial;color:rgb(34,34,34);background-color:rgb(255,255,255);font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">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.</span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:Arial;color:rgb(34,34,34);background-color:rgb(255,255,255);font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap"> </span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:Arial;color:rgb(34,34,34);background-color:rgb(255,255,255);font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap"> </span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:Arial;color:rgb(34,34,34);background-color:rgb(255,255,255);font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">Paper submission deadline: July 31, 2019</span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:Arial;color:rgb(34,34,34);background-color:rgb(255,255,255);font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap"> </span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:Arial;color:rgb(34,34,34);background-color:rgb(255,255,255);font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">Author notification: Sep 4, 2019</span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:Arial;color:rgb(34,34,34);background-color:rgb(255,255,255);font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap"> </span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:Arial;color:rgb(34,34,34);background-color:rgb(255,255,255);font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">Camera-ready deadline: Sep 25, 2019</span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:Arial;color:rgb(34,34,34);background-color:rgb(255,255,255);font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap"> </span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:Arial;color:rgb(34,34,34);background-color:rgb(255,255,255);font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">=== Submission Instructions ===</span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:Arial;color:rgb(34,34,34);background-color:rgb(255,255,255);font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap"> </span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:Arial;color:rgb(34,34,34);background-color:rgb(255,255,255);font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">Format and paper length:</span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:Arial;color:rgb(34,34,34);background-color:rgb(255,255,255);font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap"> </span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:Arial;color:rgb(34,34,34);background-color:rgb(255,255,255);font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">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:</span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:11pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap"> </span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><a href="http://www.sdlcv-workshop.com/callforpaper.html" style="text-decoration:none"><span style="font-size:11pt;font-family:Arial;color:rgb(17,85,204);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:underline;vertical-align:baseline;white-space:pre-wrap">http://www.sdlcv-workshop.com/callforpaper.html</span></a></p><br><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:Arial;color:rgb(34,34,34);background-color:rgb(255,255,255);font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">=== Invited Speakers ===</span></p><br><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:Arial;color:rgb(34,34,34);background-color:rgb(255,255,255);font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">We are proud to have a group of diverse invited speakers covering the </span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:Arial;color:rgb(34,34,34);background-color:rgb(255,255,255);font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">entire spectrum of scene and and situation understanding research:</span></p><br><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:Arial;color:rgb(34,34,34);background-color:rgb(255,255,255);font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">* Xianfeng Gu, Stony Brook University</span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:Arial;color:rgb(34,34,34);background-color:rgb(255,255,255);font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">* Alex Kendall, University of Cambridge</span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:Arial;color:rgb(34,34,34);background-color:rgb(255,255,255);font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">* Yi Ma, University of California, Berkeley</span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:Arial;color:rgb(34,34,34);background-color:rgb(255,255,255);font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">* Yingnian Wu, University of California, Los Angeles</span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:Arial;color:rgb(34,34,34);background-color:rgb(255,255,255);font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">* Alan L. Yuille, Johns Hopkins University</span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:Arial;color:rgb(34,34,34);background-color:rgb(255,255,255);font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">* Lizhong Zheng, Massachusetts Institute of Technology</span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:Arial;color:rgb(34,34,34);background-color:rgb(255,255,255);font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap"> </span></p><br><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:Arial;color:rgb(34,34,34);background-color:rgb(255,255,255);font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">=== Organizers ===</span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:Arial;color:rgb(34,34,34);background-color:rgb(255,255,255);font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap"> </span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:Arial;color:rgb(34,34,34);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">Ping Luo, HKU</span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:Arial;color:rgb(34,34,34);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">Mete Ozay, PKSHA</span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:Arial;color:rgb(34,34,34);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">Hongyang Li, CUHK</span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:Arial;color:rgb(34,34,34);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">Chaochao Lu, Cambridge University</span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:Arial;color:rgb(34,34,34);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">Lei Huang, IIAI</span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:Arial;color:rgb(34,34,34);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">Wenqi Shao, CUHK</span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:Arial;color:rgb(34,34,34);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">Xianfeng Gu, Stony Brook University</span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:Arial;color:rgb(34,34,34);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">Alan L. Yuille, Johns Hopkins University</span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:Arial;color:rgb(34,34,34);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">Xiaogang Wang, CUHK</span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:Arial;color:rgb(34,34,34);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">Yi Ma, University of California, Berkeley</span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:Arial;color:rgb(34,34,34);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">Lizhong Zheng, MIT</span></p></div>