|Title: PhD scholarship in Semi-supervised Learning for Recognizing Objects||Posted: June 12, 2014|
|Location: Grenoble, France|
Description: Many of the successful approaches for object detection, segmentation and recognition tasks rely on the availability of sufficiently large, manually annotated data for training models on a wide variety of exemplars. In the context of large-scale datasets, this is a time-consuming and an expensive requirement. Here, we will focus on methods, which learn from substantially less data. In particular we will focus on learning methods, which require only a small amount of training data available in various forms.
The goal of this PhD is to build on approaches like self-paced learning, semi-supervised learning for the object detection, segmentation and recognition tasks. This involves learning an initial model from a small labelled training set, and then refining it based on evaluations of the remainder of the training data, which is partially labelled at best. Interesting challenges of this approach are: (i) building an efficient refinement method to process large quantities of data; (ii) defining an effective evaluation procedure; (iii) incorporating cues from videos, such as optical flow, motion tracks.
- Master degree (preferably in Computer Science or Applied Mathematics. Electrical Engineering will also be considered.)
- Solid mathematics knowledge (especially linear algebra and statistics)
- Solid programming skills; the project involves programming in C
- Fluent in English, both written and spoken
- Creative and highly motivated
The research will be conducted in the LEAR team at Inria Grenoble - Rhone-Alpes. The duration of a PhD is typically 3-4 years.
Grenoble is a lively city which hosts many foreign students and researchers. Located in the heart of the French Alps, its direct surroundings offer great outdoor recreation including skiing, cycling, and hiking. Paris can be reached from Grenoble in 3h by train.
Please send applications via email to
Karteek Alahari (karteek.alahari _AT_ inria.fr) and
Cordelia Schmid (cordelia.schmid _AT_ inria.fr)
- a complete CV
- grades for Bachelors and Masters courses and thesis
- two letters of reference