# VQA-LOL: Visual Question Answering under the Lens of Logic

@article{Gokhale2020VQALOLVQ,
title={VQA-LOL: Visual Question Answering under the Lens of Logic},
author={Tejas Gokhale and Pratyay Banerjee and Chitta Baral and Yezhou Yang},
journal={ArXiv},
year={2020},
volume={abs/2002.08325}
}
Logical connectives and their implications on the meaning of a natural language sentence are a fundamental aspect of understanding. In this paper, we investigate whether visual question answering (VQA) systems trained to answer a question about an image, are able to answer the logical composition of multiple such questions. When put under this \textit{Lens of Logic}, state-of-the-art VQA models have difficulty in correctly answering these logically composed questions. We construct an…
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## References

SHOWING 1-10 OF 68 REFERENCES
We propose the task of free-form and open-ended Visual Question Answering (VQA). Given an image and a natural language question about the image, the task is to provide an accurate natural language
GloVe: Global Vectors for Word Representation
• Computer Science
EMNLP
• 2014
A new global logbilinear regression model that combines the advantages of the two major model families in the literature: global matrix factorization and local context window methods and produces a vector space with meaningful substructure.
Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering
• Computer Science
2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
• 2017
This work balances the popular VQA dataset by collecting complementary images such that every question in this balanced dataset is associated with not just a single image, but rather a pair of similar images that result in two different answers to the question.
The principle of four-cornered negation in indian philosophy
• The Review of Metaphysics pp. 694–713
• 1954
Ethics, translated by andrew boyle, introduction by ts gregory
• 1934
Ethics, translated by andrew boyle, introduction by ts gregory, 1934
• 1934
Logic-Guided Data Augmentation and Regularization for Consistent Question Answering
• Computer Science
ACL
• 2020
This paper addresses the problem of improving the accuracy and consistency of responses to comparison questions by integrating logic rules and neural models by leveraging logical and linguistic knowledge to augment labeled training data and then uses a consistency-based regularizer to train the model.
Unified Vision-Language Pre-Training for Image Captioning and VQA
• Computer Science
AAAI
• 2020
VLP is the first reported model that achieves state-of-the-art results on both vision-language generation and understanding tasks, as disparate as image captioning and visual question answering, across three challenging benchmark datasets: COCO Captions, Flickr30kCaptions, and VQA 2.0.
Video2Commonsense: Generating Commonsense Descriptions to Enrich Video Captioning
• Computer Science
EMNLP
• 2020
This work presents the first work on generating commonsense captions directly from videos, in order to describe latent aspects such as intentions, attributes, and effects, and finetune their commonsense generation models on the V2C-QA task where they ask questions about the latent aspects in the video.
A Corpus for Reasoning about Natural Language Grounded in Photographs
• Computer Science
ACL
• 2019
This work introduces a new dataset for joint reasoning about natural language and images, with a focus on semantic diversity, compositionality, and visual reasoning challenges, and Evaluation using state-of-the-art visual reasoning methods shows the data presents a strong challenge.