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<!doctype html>
<html lang="en">
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1">
<link href="bootstrap.min.css" rel="stylesheet" />
<meta name="twitter:card" content="summary_large_image" />
<!-- anon-on -->
<meta name="twitter:site" content="@GoogleAI" />
<!-- anon-off -->
<meta name="twitter:title" content="Can Pre-trained Vision and Language Models Answer Visual Information-seeking Questions?" />
<meta name="twitter:description" content="We present <i>InfoSeek</i>, a Visual Question Answering dataset that focuses on asking information-seeking questions, where the information can not be answered by common sense knowledge. We perform a multi-stage human annotation to collect a natural distribution of high-quality visual information seeking question-answer pairs. We also construct a large-scale, automatically collected dataset by combining existing visual entity recognition datasets and Wikidata, which provides over one million examples for model fine-tuning and validation." />
<link rel="preconnect" href="https://fonts.googleapis.com">
<link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
<link href="https://fonts.googleapis.com/css2?family=Roboto:wght@300&display=swap" rel="stylesheet">
<link href="https://fonts.googleapis.com/css2?family=Google+Sans:wght@400" rel="stylesheet">
<link rel="stylesheet" type="text/css" href="styles.css" />
<script src="jquery.min.js"></script>
<script src="jquery.flip.min.js"></script>
<!-- Google tag (gtag.js) -->
<script async src="https://www.googletagmanager.com/gtag/js?id=G-55TFRQJMDH"></script>
<script>
window.dataLayer = window.dataLayer || [];
function gtag(){dataLayer.push(arguments);}
gtag('js', new Date());
gtag('config', 'G-55TFRQJMDH');
</script>
<title>Can Pre-trained Vision and Language Models Answer Visual Information-seeking Questions?</title>
</head>
<body>
<div id="gallery0" style="padding-top: 50px; padding-bottom: 20px; background-color: rgb(245,245,245);">
<h2 style="text-align: center;">Can Pre-trained Vision and Language Models Answer Visual Information-Seeking Questions?</h2>
<!-- anon-on -->
<div class="authors text-center">
<div class="row">
<div class="col">Yang Chen<sup>1,2*</sup></div>
<div class="col">Hexiang Hu<sup>1</sup></div>
<div class="col">Yi Luan<sup>1</sup></div>
<div class="col">Haitian Sun<sup>1</sup></div>
</div>
<div class="row">
<div class="col">Soravit Changpinyo<sup>1</sup></div>
<div class="col">Alan Ritter<sup>2</sup></div>
<div class="col">Ming-Wei Chang<sup>1</sup></div>
</div>
<div class="row mt-3">
<div class="col">
<sup>1</sup><img src="assets/gdm-logo.svg" alt="Google Deepmind" height=35>
</div>
<div class="col">
<sup>2</sup><img src="https://brand.gatech.edu/sites/default/files/inline-images/extended-RGB.png" alt="Georgia Tech" height=45>
</div>
</div>
<div class="row">
<div class="col text-small">(*: Work done when author was interned at Google)</div>
</div>
<div class="row mt-0 nomobile">
<center><a href="https://arxiv.org/abs/2302.11713"><img class="thumbnail" src="assets/infoseek_thumbnail.png"></a></center>
</div>
</div>
<!-- anon-off -->
</div>
<div class="abstract">
<div class="inside">
<p class="text">
Large language models have demonstrated an emergent capability in answering knowledge intensive questions. With recent progress on web-scale visual and language pre-training, do these models also understand how to answer visual information seeking questions? To answer this question, we present <i>InfoSeek</i>, a Visual Question Answering dataset that focuses on asking information-seeking questions, where the information can not be answered by common sense knowledge. We perform a multi-stage human annotation to collect a natural distribution of high-quality visual information seeking question-answer pairs. We also construct a large-scale, automatically collected dataset by combining existing visual entity recognition datasets and Wikidata, which provides over one million examples for model fine-tuning and validation. Based on InfoSeek, we analyzed various pre-trained Visual QA systems to gain insights into the characteristics of different pre-trained models.
</p>
<!-- anon-on -->
<a class="read-paper" href="https://arxiv.org/abs/2302.11713"><button>Research Paper</button></a>
<a class="read-paper" href="https://github.com/open-vision-language/infoseek", target="_blank"><button>Dataset</button></a>
<a class="read-paper" href="https://github.com/edchengg/infoseek_eval", target="_blank"><button>Contributed Code</button></a>
<a class="dataset" href="https://open-vision-language.github.io/oven/", style="_blank"><button>Prior Work: OVEN</button></a>
<!-- anon-off -->
</div>
</div>
<div class="header_dark_gray text-center">
<h1><i>InfoSeek</i>, A New VQA Benchmark focuses on Visual Info-Seeking Questions </h1>
</div>
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<!-- anon-on -->
<div class="content">
<h1>Special Thanks</h1>
<p class="smaller" style="text-align: justify;"> We thank Jialin Wu, Luowei Zhou for reviewing an early version of this paper. We thank Xi Chen for providing different variants of PaLI pre-trained checkpoints. We also thank Radu Soricut, Anelia Angelova, Andre Araujo, Vittorio Ferrari for valuable discussions and feedback on the project. We thank Huiwen Chang and the Muse Team for providing their website template. Yang Chen is partially funded by the NSF (IIS-2052498).
</p>
</div>
<!-- anon-off -->
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"src": "assets/infoseek-images/aircraft.png",
"question": "Q: What is the date this aircraft took the first flight?",
"answer": "A: 1981 September 26."
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"src": "assets/infoseek-images/sport.png",
"question": "Q: Who is the founder of this sport?",
"answer": "A: Kanō Jigorō.",
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"question": "Q: What is the length of this bird in centimetre?",
"answer": "A: 120-170.",
},
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"question": "Q: Where is this animal found exclusively?",
"answer": "A: USA.",
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"question": "Q: Who is the inventor of this object?",
"answer": "A: James Gregory.",
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"question": "Q: What is the country of origin of this animal?",
"answer": "A: Rhodesia."
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"question": "Q: In which year did this building officially open?",
"answer": "A: 1930.",
},
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"question": "Q: Which year was this food invented?",
"answer": "A: 1935.",
},
{
"src": "assets/infoseek-images/lake.png",
"question": "Q: Which mountain range does this lake belong to?",
"answer": "A: Alp."
},
{
"src": "assets/infoseek-images/piano.png",
"question": "Q: What is the highest note this item can play?",
"answer": "A: C8.",
},
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"src": "assets/infoseek-images/building2.png",
"question": "Q: Which country does this building belong to?",
"answer": "A: Brazil."
},
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"question": "Q: Where is this plant native to?",
"answer": "A: Ecuador."
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