08:00 Welcome and Agenda Presentation
08:15 Spotlight Presentation of Workshop Papers. 5 minutes each:
Paper 1: “Curriculum Learning for Multi-Task Classification of Visual Attributes”
Nikolaos Sarafianos; Theodore Giannakopoulos; Christophoros Nikou; Ioannis Kakadiaris
Paper 2: “Zero-Shot Learning posed as a Missing Data Problem”
Bo Zhao; Botong Wu; Tianfu Wu; Yizhou Wang
Paper 3: “Deep Modality Invariant Adversarial Network for Shared Representation Learning”
Kuniaki Saito; Yusuke Mukuta; Yoshitaka Ushiku; Tatsuya Harada
Paper 4: “Discrepancy-based networks for unsupervised domain adaptation: a comparative study”
Gabriela Csurka; Fabien Baradel; Boris Chidlovskii; Stephane Clinchant
Paper 5: “Adaptive SVM+: Learning with Privileged Information for Domain Adaptation”
Nikolaos Sarafianos; Michalis Vrigkas; Ioannis Kakadiaris
Poster 6: “Deep Depth Domain Adaptation: A Case Study”
Novi Patricia; Fabio M. Carlucci; Barbara Caputo
Paper 7: “Deep Domain Adaptation by Geodesic Distance Minimization”
Yifei Wang; Wen Li; Dengxin Dai; Luc Van Gool
Paper 8: “Unsupervised Cross-Domain Image Hashing with Adversarial Learning”
Liu Liu*; Hairong Qi
Paper 9: “Inferring Human Activities Using Robust Privileged Probabilistic Learning”
Michalis Vrigkas; Evangelos Kazakos; Christophoros Nikou; Ioannis Kakadiaris
09:00 Invited Talk 1: Adaptive Deep Learning at UC Berkeley and Nexar”, Prof. Trevor Darrell
Learning of layered or “deep” representations has provided significant advances in computer vision in recent years, but has traditionally been limited to fully supervised settings with very large amounts of training data.  New results in adversarial adaptive representation learning show how such methods can also excel when learning  across modalities and domains. I’ll present recent long-term recurrent network models that learn visuomotor policies including end to end driving from large scale crowdsourced datasets. The models leverage recently released datasets from Nexar; I’ll review these and will also present the recent Nexar recognition challenge winning entries.
09:45 Poster Session and Coffee Break
 11:15  Oral 1 – Best Paper Award winner: 
“Generating Visual Representations for Zero-Shot Classification”
Maxime Bucher; Stephane Herbin; Frederic Jurie
11:35 Oral 2 – Honorable Mention paper:  
“Exploiting Convolution Filter Patterns for Transfer Learning”
Mehmet Aygün; Yusuf Aytar; Hazim Ekenel
11:50 Invited Talk 2: Unsupervised Pixel-level Domain Adaptation with Generative Adversarial Networks”, Dr. Dilip Krishnan
Collecting well-annotated image datasets to train modern machine learning algorithms is prohibitively expensive for many tasks. One appealing alternative is rendering synthetic data where ground-truth annotations are generated automatically. Unfortunately, models trained purely on rendered images often fail to generalize to real images. To address this shortcoming, prior work introduced unsupervised domain adaptation algorithms that attempt to map representations between the two domains or learn to extract features that are domain-invariant. In this work, we present a new approach that learns, in an unsupervised manner, a transformation in the pixel space from one domain to the other. Our generative adversarial network (GAN)-based method adapts source-domain images to appear as if drawn from the target domain. Our approach not only produces plausible samples, but also outperforms the state-of-the-art on a number of unsupervised domain adaptation scenarios by large margins. Finally, we demonstrate that the adaptation process generalizes to object classes unseen during training.
12:35 Lunch
14:00 Invited Talk 3: “Domain Adaptation for Visual Applications”, Dr. Gabriela Csurka
The aim of my talk will be to give an overview of visual domain adaptation methods. The first part will focus on the tendencies used by historical shallow methods. Then I will discuss the effect of the success of deep convolutional architectures on the field of domain adaption showing that domain adaptation can benefit from these architectures in various manner. At the end of my talk I will briefly discuss domain adaptation for applications beyond image categorization.
14:45 VisDA Challenge Introduction
14:55 VisDA Classification Challenge: Honorable Mention Talk
15:05 VisDA Classification Challenge: Runner-Up Talk
15:15 VisDA Classification Challenge: Winner Talk
15:25 Afternoon Break
16:00  VisDA Segmentation Challenge: Honorable Mention Talk
16:10  VisDA Segmentation Challenge: Runner-Up Talk
16:20  VisDA Segmentation Challenge: Winner Talk
16:30 Best paper announcement and Workshop closing