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A significant gap remains between today's visual pattern recognition modelsand human-level visual cognition especially when it comes to few-shot learningand compositional reasoning of novel concepts. We introduce Bongard-HOI, a newvisual reasoning benchmark that focuses on compositional learning ofhuman-object interactions (HOIs) from natural images. It is inspired by twodesirable characteristics from the classical Bongard problems (BPs): 1)few-shot concept learning, and 2) context-dependent reasoning. We carefullycurate the few-shot instances with hard negatives, where positive and negativeimages only disagree on action labels, making mere recognition of objectcategories insufficient to complete our benchmarks. We also design multipletest sets to systematically study the generalization of visual learning models,where we vary the overlap of the HOI concepts between the training and testsets of few-shot instances, from partial to no overlaps. Bongard-HOI presents asubstantial challenge to today's visual recognition models. Thestate-of-the-art HOI detection model achieves only 62% accuracy on few-shotbinary prediction while even amateur human testers on MTurk have 91% accuracy.With the Bongard-HOI benchmark, we hope to further advance research efforts invisual reasoning, especially in holistic perception-reasoning systems andbetter representation learning.

Bongard-HOI: Benchmarking Few-Shot Visual Reasoning for Human-Object Interactions