Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/126704
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Type: Conference paper
Title: Actively seeking and learning from live data
Author: Teney, D.
Hengel, A.V.D.
Citation: Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2019), 2019 / vol.2019-June, pp.1940-1949
Publisher: Computer Vision Foundation / IEEE
Publisher Place: online
Issue Date: 2019
Series/Report no.: IEEE Conference on Computer Vision and Pattern Recognition
ISBN: 9781728132938
ISSN: 1063-6919
2575-7075
Conference Name: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (15 Jun 2019 - 20 Jun 2019 : Long Beach, USA)
Statement of
Responsibility: 
Damien Teney, Anton van den Hengel
Abstract: One of the key limitations of traditional machine learning methods is their requirement for training data that exemplifies all the information to be learned. This is a particular problem for visual question answering methods, which may be asked questions about virtually anything. The approach we propose is a step toward overcoming this limitation by searching for the information required at test time. The resulting method dynamically utilizes data from an external source, such as a large set of questions/answers or images/captions. Concretely, we learn a set of base weights for a simple VQA model, that are specifically adapted to a given question with the information specifically retrieved for this question. The adaptation process leverages recent advances in gradient-based meta learning and contributions for efficient retrieval and cross-domain adaptation. We surpass the state-of-the-art on the VQACP v2 benchmark and demonstrate our approach to be intrinsically more robust to out-of-distribution test data. We demonstrate the use of external non-VQA data using the MS COCO captioning dataset to support the answering process. This approach opens a new avenue for open-domain VQA systems that interface with diverse sources of data.
Rights: ©2019 IEEE
RMID: 1000011452
DOI: 10.1109/CVPR.2019.00204
Published version: http://openaccess.thecvf.com/CVPR2019.py
Appears in Collections:Australian Institute for Machine Learning publications

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