Name
Title | ||
|---|---|---|
Knowing Earlier What Right Means to You: A Comprehensive VQA Dataset for Grounding Relative Directions via Multi-task Learning. |
Abstract | ||
|---|---|---|
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 relative directions. 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. |
Year | Venue | DocType |
|---|---|---|
2022 | International Joint Conference on Artificial Intelligence | Conference |
ISSN | Citations | PageRank |
IJCAI 2022 Workshop on Spatio-Temporal Reasoning and Learning | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kyra Ahrens | 1 | 0 | 1.01 |
| Matthias Kerzel | 2 | 0 | 1.01 |
| Jae Hee Lee | 3 | 67 | 8.50 |
| Cornelius Weber | 4 | 318 | 41.92 |
| Stefan Wermter | 5 | 1100 | 151.62 |