English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Psychologists recognize Raven's Progressive Matrices as a very effective testof general human intelligence. While many computational models have beendeveloped by the AI community to investigate different forms of top-down,deliberative reasoning on the test, there has been less research on bottom-upperceptual processes, like Gestalt image completion, that are also critical inhuman test performance. In this work, we investigate how Gestalt visualreasoning on the Raven's test can be modeled using generative image inpaintingtechniques from computer vision. We demonstrate that a self-supervisedinpainting model trained only on photorealistic images of objects achieves ascore of 27/36 on the Colored Progressive Matrices, which corresponds toaverage performance for nine-year-old children. We also show that modelstrained on other datasets (faces, places, and textures) do not perform as well.Our results illustrate how learning visual regularities in real-world imagescan translate into successful reasoning about artificial test stimuli. On theflip side, our results also highlight the limitations of such transfer, whichmay explain why intelligence tests like the Raven's are often sensitive topeople's individual sociocultural backgrounds.

Modeling Gestalt Visual Reasoning on the Raven's Progressive Matrices Intelligence Test Using Generative Image Inpainting Techniques