Bongard-OpenWorld: Few-Shot Reasoning for Free-form Visual Concepts in the Real World

TL;DR

Bongard-OpenWorld is a challenging new benchmark for evaluating few-shot reasoning on real-world, open-vocabulary visual concepts, highlighting current limitations of AI models compared to human performance.

Contribution

The paper introduces Bongard-OpenWorld, a novel benchmark for open-world, few-shot visual reasoning with real images, and explores various AI approaches, revealing significant gaps with human reasoning.

Findings

Current models achieve 64% accuracy, below human 91%.

Open-world, free-form concepts significantly challenge existing algorithms.

Neuro-symbolic reasoning approaches show potential but still fall short.

Abstract

We introduce Bongard-OpenWorld, a new benchmark for evaluating real-world few-shot reasoning for machine vision. It originates from the classical Bongard Problems (BPs): Given two sets of images (positive and negative), the model needs to identify the set that query images belong to by inducing the visual concepts, which is exclusively depicted by images from the positive set. Our benchmark inherits the few-shot concept induction of the original BPs while adding the two novel layers of challenge: 1) open-world free-form concepts, as the visual concepts in Bongard-OpenWorld are unique compositions of terms from an open vocabulary, ranging from object categories to abstract visual attributes and commonsense factual knowledge; 2) real-world images, as opposed to the synthetic diagrams used by many counterparts. In our exploration, Bongard-OpenWorld already imposes a significant challenge to current few-shot reasoning algorithms. We further investigate to which extent the recently introduced Large Language Models (LLMs) and Vision-Language Models (VLMs) can solve our task, by directly probing VLMs, and combining VLMs and LLMs in an interactive reasoning scheme. We even conceived a neuro-symbolic reasoning approach that reconciles LLMs & VLMs with logical reasoning to emulate the human problem-solving process for Bongard Problems. However, none of these approaches manage to close the human-machine gap, as the best learner achieves 64% accuracy while human participants easily reach 91%. We hope Bongard-OpenWorld can help us better understand the limitations of current visual intelligence and facilitate future research on visual agents with stronger few-shot visual reasoning capabilities.