A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf
.
Filters
WeaQA: Weak Supervision via Captions for Visual Question Answering
[article]
2021
arXiv
pre-print
Methodologies for training visual question answering (VQA) models assume the availability of datasets with human-annotated Image-Question-Answer (I-Q-A) triplets. ...
We present a method to train models with synthetic Q-A pairs generated procedurally from captions. ...
Acknowledgements The authors acknowledge support from the DARPA SAIL-ON program W911NF2020006, ONR award N00014-20-1-2332, and NSF grant 1816039, and the anonymous reviewers for their insightful discussion ...
arXiv:2012.02356v2
fatcat:yoqklfrx2vhctm7u24elycwwsi
Weakly-Supervised Visual-Retriever-Reader for Knowledge-based Question Answering
[article]
2021
arXiv
pre-print
Knowledge-based visual question answering (VQA) requires answering questions with external knowledge in addition to the content of images. ...
Both the retriever and reader are trained with weak supervision. Our experimental results show that a good retriever can significantly improve the reader's performance on the OK-VQA challenge. ...
Acknowledgements The authors acknowledge support from the NSF grant 1816039, DARPA grant W911NF2020006, DARPA grant FA875019C0003, and ONR award N00014-20-1-2332; and thank the reviewers for their feedback ...
arXiv:2109.04014v1
fatcat:rnm2ghrosbd4xkctt4jnozfndu
Low-resource Learning with Knowledge Graphs: A Comprehensive Survey
[article]
2021
arXiv
pre-print
In this survey, we very comprehensively reviewed over 90 papers about KG-aware research for two major low-resource learning settings – zero-shot learning (ZSL) where new classes for prediction have never ...
KG curation (e.g., inductive KG completion), and some typical evaluation resources for each task. ...
WeaQA: Weak supervision via captions for visual question answering. In
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021. 3420–3435. ...
arXiv:2112.10006v3
fatcat:wkz6gjx4r5gvlhh673p3rqsmgi