# The Color of the Cat is Gray: 1 Million Full-Sentences Visual Question Answering (FSVQA)

Source: [The Color of the Cat is Gray: 1 Million Full-Sentences Visual Question Answering (FSVQA)](<https://www.sota2.com/research/paper/68fa62bb1e209131ce9faebe>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-27T05:51:52.529Z

Authors: Andrew Shin, Yoshitaka Ushiku, Tatsuya Harada

Published: 2016-09-21T18:12:04.000Z

## Abstract

Visual Question Answering (VQA) task has showcased a new stage of interaction between language and vision, two of the most pivotal components of artificial intelligence. However, it has mostly focused on generating short and repetitive answers, mostly single words, which fall short of rich linguistic capabilities of humans. We introduce Full-Sentence Visual Question Answering (FSVQA) dataset, consisting of nearly 1 million pairs of questions and full-sentence answers for images, built by applying a number of rule-based natural language processing techniques to original VQA dataset and captions in the MS COCO dataset. This poses many additional complexities to conventional VQA task, and we provide a baseline for approaching and evaluating the task, on top of which we invite the research community to build further improvements.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1609.06657>)

[PDF](<https://arxiv.org/pdf/1609.06657.pdf>)