Abstract Visual Reasoning with Tangram Shapes
Anya Ji | Noriyuki Kojima | Noah Rush | Alane Suhr | Wai Keen Vong | Robert Hawkins | Yoav Artzi
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
We introduce KiloGram, a resource for studying abstract visual reasoning in humans and machines. Drawing on the history of tangram puzzles as stimuli in cognitive science, we build a richly annotated dataset that, with >1k distinct stimuli, is orders of magnitude larger and more diverse than prior resources. It is both visually and linguistically richer, moving beyond whole shape descriptions to include segmentation maps and part labels. We use this resource to evaluate the abstract visual reasoning capacities of recent multi-modal models. We observe that pre-trained weights demonstrate limited abstract reasoning, which dramatically improves with fine-tuning. We also observe that explicitly describing parts aids abstract reasoning for both humans and models, especially when jointly encoding the linguistic and visual inputs.
- Noriyuki Kojima 1
- Noah Rush 1
- Alane Suhr 1
- Wai Keen Vong 1
- Robert Hawkins 1
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