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

Nancy J. Delong

Present visible dilemma answering datasets focus on pure pictures. Even so, summary diagrams with visible and semantic richness account for a significant proportion of the visible world.

An summary diagram. Picture credit rating: Pxhere, CC0 Community Area

A recent examine proposes Icon Problem Answering, a new challenge for summary diagram visible reasoning and dilemma answering.

The activity stems from math phrase problems for small children and displays a promising possible to establish training assistants. A significant-scale dataset that contains 107,439 QA pairs and masking three different sub-responsibilities: various-graphic-decision, various-text-decision, and filling-in-the-blank is launched. The right way answering these questions calls for varied skills, like recognizing objects, pinpointing characteristics, earning sensible inferences, or completing spatial reasoning.

The dataset is benchmarked thoroughly by way of experiments on eight current approaches, and a sturdy multimodal Transformer-centered baseline is made.

Present visible dilemma answering (VQA) responsibilities mainly contemplate answering human-annotated questions for pure pictures. Even so, apart from pure pictures, summary diagrams with semantic richness are still understudied in visible comprehending and reasoning investigate. In this operate, we introduce a new challenge of Icon Problem Answering (IconQA) with the aim of answering a dilemma in an icon graphic context. We launch IconQA, a significant-scale dataset that consists of 107,439 questions and three sub-responsibilities: multi-graphic-decision, multi-text-decision, and filling-in-the-blank. The IconQA dataset is influenced by serious-world diagram phrase problems that highlight the value of summary diagram comprehending and comprehensive cognitive reasoning. Thus, IconQA calls for not only perception skills like object recognition and text comprehending, but also varied cognitive reasoning skills, these as geometric reasoning, commonsense reasoning, and arithmetic reasoning. To aid possible IconQA types to master semantic representations for icon pictures, we further launch an icon dataset Icon645 which consists of 645,687 colored icons on 377 lessons. We carry out considerable user studies and blind experiments and reproduce a extensive selection of advanced VQA approaches to benchmark the IconQA activity. Also, we establish a sturdy IconQA baseline Patch-TRM that applies a pyramid cross-modal Transformer with enter diagram embeddings pre-properly trained on the icon dataset. IconQA and Icon645 are out there at this https URL.

Research paper: Lu, P., “IconQA: A New Benchmark for Abstract Diagram Knowledge and Visual Language Reasoning”, 2021. Backlink: https://arxiv.org/ab muscles/2110.13214


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