Knowing Earlier what Right Means to You: A Comprehensive VQA Dataset for Grounding Relative Directions via Multi-Task Learning
IJCAI Workshop on Spatio-Temporal Reasoning and Learning,
- Jul 2022
Associated documents :
<!DOCTYPE html PUBLIC "-//W3C//DTD HTML 4.01//EN" "http://www.w3.org/TR/html4/strict.dtd">
<html>
<head>
<meta http-equiv="Content-Type" content="text/html; charset=utf-8">
<meta http-equiv="Content-Style-Type" content="text/css">
<title></title>
<meta name="Generator" content="Cocoa HTML Writer">
<meta name="CocoaVersion" content="2113.4">
<style type="text/css">
p.p1 {margin: 0.0px 0.0px 0.0px 0.0px; font: 14.0px Optima; color: #000000; -webkit-text-stroke: #000000}
span.s1 {font-kerning: none}
</style>
</head>
<body>
<p class="p1"><span class="s1">Spatial reasoning poses a particular challenge for intelligent agents and is at the same time a prerequisite for their successful interaction and communication in the physical world. One such reasoning task is to describe the position of a target object with respect to the intrinsic orientation of some reference object via <i>relative directions</i>. In this paper, we introduce GRiD-A-3D, a novel diagnostic visual question-answering (VQA) dataset based on abstract objects. Our dataset allows for a fine-grained analysis of end-to-end VQA models' capabilities to ground relative directions. At the same time, model training requires considerably fewer computational resources compared with existing datasets, yet yields a comparable or even higher performance. Along with the new dataset, we provide a thorough evaluation based on two widely known end-to-end VQA architectures trained on GRiD-A-3D. We demonstrate that within a few epochs, the subtasks required to reason over relative directions, such as recognizing and locating objects in a scene and estimating their intrinsic orientations, are learned in the order in which relative directions are intuitively processed.</span></p>
</body>
</html>
@InProceedings{AKLWW22,
author = {Ahrens, Kyra and Kerzel, Matthias and Lee, Jae Hee and Weber, Cornelius and Wermter, Stefan},
title = {Knowing Earlier what Right Means to You: A Comprehensive VQA Dataset for Grounding Relative Directions via Multi-Task Learning},
booktitle = {IJCAI Workshop on Spatio-Temporal Reasoning and Learning},
editors = {},
number = {},
volume = {},
pages = {},
year = {2022},
month = {Jul},
publisher = {},
doi = {},
}