Sponsored by


Jose Alvarez

Call for Papers

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9:15 Welcome
9:20 Kevin Murphy (Google)
10:00 Morning Break
10:30 Josef Sivic (INRIA)
11:05 Adriana Romero (Facebook AI)
11:40 Olga Russakovsky (Princeton)
12:25 Lunch
14:00 Vittorio Ferrari (Google)
14:35 Chris Re (Stanford)
15:10 Devi Parik (Georgia Tech and Facebook AI)
15:45 Afternoon Break & Poster Session
List of Extended Abstracts (Posters)
A probabilistic constrained clustering for transfer learning and image category discovery, Yen-Chang Hsu, Zhaoyang Lv, Joel Schlosser, Phillip Odom, Zsolt Kira
Near-field Depth Estimation using Monocular Fisheye Camera: A Semi-supervised learning approach using Sparse Velodyne Data, Varun Ravi Kumar, Stefan Milz, Martin Simon, Christian Witt, Karl Amende, Johannes Petzold, Senthil Yogamani, Timo Pech
Comparison of Deep Learning Models for Semantic Segmentation on Domain Specific Data in Food Processing, Nicolas Loerbroks, Piyawat Suwanvithaya; Isabel Schwende
Material Segmentation from Local Appearance and Global Context, Gabriel Schwartz, Ko Nishino
Fusion Scheme for Semantic and Instance-level Segmentation, Arthur Costea, Andra Petrovai, Sergiu Nedevschi
Weakly Supervised Object Localization via Sensitivity Analysis, Mohammad K. Ebrahimpour, David C. Noelle
A Multi-Layer Approach to Superpixel-based Higher-order Conditional Random Field for Semantic Image Segmentation, Li Sulimowicz, Ishfaq Ahmad, alexander aved
Two Stream Self-Supervised Learning for Action Recognition, Ahmed Taha, Moustafa Meshry, Xitong Yang, Yi-Ting Chen, Larry Davis
Object Detection using Domain Randomization and Generative Adversarial Refinement of Synthetic Images, Fernando Camaro Nogues, Andrew Huie, Sakya Dasgupta
Scaling Neural Programmer-Interpreter For Real-Life Tasks, Himadri Mishra, K K Shukla
Fast and Light-weight Unsupervised Depth Estimation for Mobile GPU Hardware, Sangyun Oh, Jongeun Lee, Hye-Jin S. Kim
Semi-supervised Learning: Fusion of Self-supervised, Supervised Learning, and Multimodal Cues for Tactical Driver Behavior Detection, Athmanarayanan Lakshmi narayanan, Yi-Ting Chen, Srikanth Malla
Recurrent Neural Networks for Semantic Instance Segmentation, Amaia Salvador, Míriam Bellver, Manel Baradad, Victor Campos, Ferran Marques, Jordi Torres, Xavier Giro-i-Nieto
Action2Vec: A Crossmodal Embedding Approach to Zero Shot Action Learning, Meera Hahn, Andrew Silva, James M. Rehg
Generating superpixels with deep representations, Thomas Verelst, Maxim Berman, Matthew B. Blaschko

Invited Speakers

Organizing Commitee

Description of the workshop

Most of the major advances in Deep Learning have come from supervised learning. Despite these successes, supervised learning algorithms are characterized by a major limitation: they necessitate massive amounts of carefully, and typically expensively, annotated data. This workshop will emphasis future directions beyond supervised learning such as reinforcement learning and weakly supervised learning. Such approaches require far less supervision and allow computers to learn beyond mimicking what is explicitly encoded in a large-scale set of annotations. We encourage researchers to formulate innovative learning theories, feature representations, and end-to-end vision systems based on deep learning. We also encourage new theories and processes for dealing with large scale image datasets through deep learning architectures. We are soliciting original contributions that address a wide range of theoretical and practical issues including, but not limited to:

As main difference with previous years, for this edition of the workshop, papers are meant to be extended abstracts showing current / preliminary / novel results to encourage discussion during the workshop.