Call for Extended Abstracts (2 pages)Submission site: https://cmt3.research.microsoft.com/BV2016
Submission deadline: April 4th, 2016
Big data has been revolutionizing computer vision research. The increasing availability of massive visual datasets creates unprecedented opportunities for researchers to tackle fundamental computer vision challenges: recognizing everything in the visual world, indexing and organizing the sea of visual information, and extracting knowledge and discovering patterns from big visual data. Achieving these goals calls for bold innovations on many fronts: data collection, learning, inference, representations, indexing, and systems infrastructure.
The goal of this workshop is providing a venue for researchers interested in large-scale vision to present new work, exchange ideas, and connect with each other. The workshop will feature invited talks from leading researchers as well as a poster session that fosters in depth discussion.
We invite submissions of extended abstracts related to the following topics in the context of big data and large-scale vision:
- Indexing algorithms and data structures
- Weakly supervised or unsupervised learning
- Metric learning
- Visual models and feature representations
- Transfer learning and domain adaptation
- Systems and infrastructure
- Visual data mining and knowledge discovery
- Dataset issues (e.g. dataset collection and dataset biases)
- Efficient learning and inference techniques
- Optimization techniques
The abstracts should be no more than 2 pages in CVPR 2016 format. Accepted abstracts will be presented as posters. The workshop is not intended as a venue for publication and no proceedings will be produced. All submissions will undergo double-blind reviews. In the case of previous published work, the review will be single-blind.