TY - GEN
T1 - Straight to the facts
T2 - 15th European Conference on Computer Vision, ECCV 2018
AU - Narasimhan, Medhini
AU - Schwing, Alexander G.
N1 - Funding Information:
Acknowledgments. This material is based upon work supported in part by the National Science Foundation under Grant No. 1718221, Samsung, and 3M. We thank NVIDIA for providing the GPUs used for this research. We also thank Arun Mallya and Aditya Deshpande for their help.
PY - 2018
Y1 - 2018
N2 - Question answering is an important task for autonomous agents and virtual assistants alike and was shown to support the disabled in efficiently navigating an overwhelming environment. Many existing methods focus on observation-based questions, ignoring our ability to seamlessly combine observed content with general knowledge. To understand interactions with a knowledge base, a dataset has been introduced recently and keyword matching techniques were shown to yield compelling results despite being vulnerable to misconceptions due to synonyms and homographs. To address this issue, we develop a learning-based approach which goes straight to the facts via a learned embedding space. We demonstrate state-of-the-art results on the challenging recently introduced fact-based visual question answering dataset, outperforming competing methods by more than 5%.
AB - Question answering is an important task for autonomous agents and virtual assistants alike and was shown to support the disabled in efficiently navigating an overwhelming environment. Many existing methods focus on observation-based questions, ignoring our ability to seamlessly combine observed content with general knowledge. To understand interactions with a knowledge base, a dataset has been introduced recently and keyword matching techniques were shown to yield compelling results despite being vulnerable to misconceptions due to synonyms and homographs. To address this issue, we develop a learning-based approach which goes straight to the facts via a learned embedding space. We demonstrate state-of-the-art results on the challenging recently introduced fact-based visual question answering dataset, outperforming competing methods by more than 5%.
KW - Fact based visual question answering
KW - Knowledge bases
UR - http://www.scopus.com/inward/record.url?scp=85055455328&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85055455328&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-01237-3_28
DO - 10.1007/978-3-030-01237-3_28
M3 - Conference contribution
AN - SCOPUS:85055455328
SN - 9783030012366
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 460
EP - 477
BT - Computer Vision – ECCV 2018 - 15th European Conference, 2018, Proceedings
A2 - Ferrari, Vittorio
A2 - Sminchisescu, Cristian
A2 - Weiss, Yair
A2 - Hebert, Martial
PB - Springer-Verlag Berlin Heidelberg
Y2 - 8 September 2018 through 14 September 2018
ER -