Abstract
As Computer Vision moves from passive analysis of pixels to active analysis of semantics, the breadth of information algorithms need to reason over has expanded significantly. One of the key challenges in this vein is the ability to identify the information required to make a decision, and select an action that will recover it. We propose a reinforcement-learning approach that maintains a distribution over its internal information, thus explicitly representing the ambiguity in what it knows, and needs to know, towards achieving its goal. Potential actions are then generated according to this distribution. For each potential action a distribution of the expected outcomes is calculated, and the value of the potential information gain assessed. The action taken is that which maximizes the potential information gain. We demonstrate this approach applied to two vision-and-language problems that have attracted significant recent interest, visual dialog and visual query generation. In both cases the method actively selects actions that will best reduce its internal uncertainty, and outperforms its competitors in achieving the goal of the challenge.
Original language | English |
---|---|
Title of host publication | Proceedings - 33th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2020 |
Editors | Ce Liu, Greg Mori, Kate Saenko |
Place of Publication | Piscataway NJ USA |
Publisher | IEEE, Institute of Electrical and Electronics Engineers |
Pages | 13447-13456 |
Number of pages | 10 |
ISBN (Electronic) | 9781728171685 |
ISBN (Print) | 9781728171692 |
DOIs | |
Publication status | Published - 2020 |
Externally published | Yes |
Event | IEEE Conference on Computer Vision and Pattern Recognition 2020 - Virtual, China Duration: 14 Jun 2020 → 19 Jun 2020 http://cvpr2020.thecvf.com (Website ) https://openaccess.thecvf.com/CVPR2020 (Proceedings) https://ieeexplore.ieee.org/xpl/conhome/9142308/proceeding (Proceedings) |
Publication series
Name | Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition |
---|---|
Publisher | IEEE, Institute of Electrical and Electronics Engineers |
ISSN (Print) | 1063-6919 |
ISSN (Electronic) | 2575-7075 |
Conference
Conference | IEEE Conference on Computer Vision and Pattern Recognition 2020 |
---|---|
Abbreviated title | CVPR 2020 |
Country/Territory | China |
City | Virtual |
Period | 14/06/20 → 19/06/20 |
Internet address |
|