- Deadline for extended abstract submissions: 25th of July 2018
- Acceptance notification: 1st of August 2018
- Workshop: 14th of September 2018
|09:00am - 09:10am||
Welcome and Introduction
|09:10am - 09:40am||
Tinne Tuytelaars (KU Leuven) - "Incremental learning: a critical view on the current state of affairs"
|09:45am - 10:15am||
Manuel Günther - “Results and Evaluation of the Open-Face Challenge” (challenge website)
|10:15am - 10:45am||
|10:45am - 11:15am||
Kristen Grauman (UT Austin) - “Recognition with unseen compositions and novel environments“
|11:20am - 11:50am||
Jordi Pont-Tuset (Google AI) - “Interactive video segmentation: The DAVIS benchmark and first approaches”
|11:50am - 12:30am||
|12:30am - 02:00pm||
|02:00pm - 02:30pm||
Christoph Lampert (IST Austria) - "Towards continual learning and interactive annotation"
|02:35pm - 03:05pm||
Joachim Denzler (Univ. Jena) - “Elements of Continuous Learning for Wildlife Monitoring“
|Lawrence Neal, Matthew Olson, Xiaoli Fern, Weng-Keen Wong, Fuxin Li||Open Set Learning with Counterfactual Images|
|Manuel Guenther, Walter Scheirer, Terry Boult||Open-Set Recognition Challenge|
|Pau Panareda Busto and Juergen Gall||Open Set Domain Adaptation for Image and Action Recognition|
|Kshitij Dwivedi and Gemma Roig||Evaluation of plug and play modules for multi-domain learning|
|SouYoung Jin and Aruni RoyChowdhury and Huaizu Jiang and Ashish Singh and Aditya Prasad and Deep Chakraborty and Erik Learned-Miller||Unsupervised Hard Example Mining from Videos for Improved Object Detection|
|Aljosa Osep and Paul Voigtlaender and Jonathon Luiten and Stefan Breuers and Bastian Leibe||Towards Large-Scale Video Object Mining|
|Lisa Wang and Ranti Dev Sharma||Unsupervised Representation Learning on Multispectral Imagery By Predicting Held-Out Bands|
|Ranti Dev Sharma and Lisa Wang||Human-in-the-loop segmentation for improved segmentation and annotations|
|Hartmut Bauermeister and Peter Ochs and Tim Meinhardt and Laura Leal-Taixe and Michael Moeller||Adaptive Network Architectures via Linear Splines|
|Kate Rakelly* and Evan Shelhamer* and Trevor Darrell and Alexei A. Efros and Sergey Levine||Few-Shot Segmentation Propagation with Guided Networks|
Learning algorithms are the backbone of computer vision research and still focused on training from large amounts of already annotated data. The limitations we are currently observing in many applications are mostly due to the lack of annotations or changing data distributions over time. To overcome these barriers, the annotation and learning of models needs to be coupled strongly through human-machine interaction. Furthermore, models need to adapt as needed to handle either shifts or completely novel data. The goal of this workshop is to discuss and present the advances in technologies that support annotation, model learning through expert guidance and continuous model adaptation.
The abstract will not appear in any proceedings and if accepted only appear online on this page (if authors like). Our workshop is not meant as a publication venue, but rather a real meeting, where you learn about people interested in the same area and find the next cooperation partners for your future project.
Accepted abstracts will be presented in a quick teaser or poster. We also welcome submissions of industrial partners interested in the topic and willing to present their application area. Furthermore, if you want to present your next proposal idea and you are looking for cooperation partners, you are also very much invited to submit an abstract.