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Current visual question answering (VQA) tasks mainly consider answeringhuman-annotated questions for natural images. However, aside from naturalimages, abstract diagrams with semantic richness are still understudied invisual understanding and reasoning research. In this work, we introduce a newchallenge of Icon Question Answering (IconQA) with the goal of answering aquestion in an icon image context. We release IconQA, a large-scale datasetthat consists of 107,439 questions and three sub-tasks: multi-image-choice,multi-text-choice, and filling-in-the-blank. The IconQA dataset is inspired byreal-world diagram word problems that highlight the importance of abstractdiagram understanding and comprehensive cognitive reasoning. Thus, IconQArequires not only perception skills like object recognition and textunderstanding, but also diverse cognitive reasoning skills, such as geometricreasoning, commonsense reasoning, and arithmetic reasoning. To facilitatepotential IconQA models to learn semantic representations for icon images, wefurther release an icon dataset Icon645 which contains 645,687 colored icons on377 classes. We conduct extensive user studies and blind experiments andreproduce a wide range of advanced VQA methods to benchmark the IconQA task.Also, we develop a strong IconQA baseline Patch-TRM that applies a pyramidcross-modal Transformer with input diagram embeddings pre-trained on the icondataset. IconQA and Icon645 are available at https://iconqa.github.io.

IconQA: A New Benchmark for Abstract Diagram Understanding and Visual Language Reasoning