Description
Deep convolutional networks have become the go-to technique for a variety of computer vision task such as image classification, object detection, segmentation, key points detection, etc. These over-parameterized models are known to be data-hungry; tens of thousand of labelled examples are typically required. Since manual annotations are expensive, learning from “weaker” annotations (e.g. only image-level category labels to localize object instances by a bounding box) become key to expand the success of deep networks to new applications.
This tutorial will provide an overview of weakly supervised learning methods in computer vision, and we will discuss the broad area of weakly supervised object recognition and its limitations of current state-of-the-art, evaluation metrics, and future ideas that will spur disruptive progress in the field of weakly supervised learning.
This tutorial will provide an overview of weakly supervised learning methods in computer vision, and we will discuss the broad area of weakly supervised object recognition and its limitations of current state-of-the-art, evaluation metrics, and future ideas that will spur disruptive progress in the field of weakly supervised learning.
Speakers
University of Edinburgh
Schedule
June 18, Room 151 ABC
09.00. Introduction, by Jasper Uijlings
09.15. Weakly supervised object detection by Hakan Bilen
10.00. Morning break
10.30. Weakly supervised semantic segmentation by Rodrigo Benenson
11.15. Less weakly supervised object detection and segmentation by Jasper Uijlings
12.00. Closing remarks by Rodrigo Benenson
09.15. Weakly supervised object detection by Hakan Bilen
10.00. Morning break
10.30. Weakly supervised semantic segmentation by Rodrigo Benenson
11.15. Less weakly supervised object detection and segmentation by Jasper Uijlings
12.00. Closing remarks by Rodrigo Benenson