RAVEN consists of 1,120,000 images and 70,000 RPM (Raven's Progressive Matrices) problems, equally distributed in 7 distinct figure configurations.
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AbstractReasoning is a dataset for abstract reasoning, where the goal is to infer the correct answer from the context panels based on abstract reasoning.
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PGM dataset serves as a tool for studying both abstract reasoning and generalisation in models. Generalisation is a multi-faceted phenomenon; there is no single, objective way in which models can or should generalise beyond their experience. The PGM dataset provides a means to measure the generalization ability of models in different ways, each of which may be more or less interesting to researchers depending on their intended training setup and applications.
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The People in Social Context (PISC) dataset is a dataset that focuses on social relationships. It consists of 22,670 images of 9 types of social relationships. It has annotations for the bounding boxes of all people, as well as the social relationship between all pairs of people in the images. In addition, it also contains occupation annotation.
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Sequence Consistency Evaluation (SCE) consists of a benchmark task for sequence consistency evaluation (SCE).
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Green family of datasets for emergent communications on relations.
A fundamental component of human vision is our ability to parse complex visual scenes and judge the relations between their constituent objects. AI benchmarks for visual reasoning have driven rapid progress in recent years with state-of-the-art systems now reaching human accuracy on some of these benchmarks. Yet, there remains a major gap between humans and AI systems in terms of the sample efficiency with which they learn new visual reasoning tasks. Humans' remarkable efficiency at learning has been at least partially attributed to their ability to harness compositionality -- allowing them to efficiently take advantage of previously gained knowledge when learning new tasks. Here, we introduce a novel visual reasoning benchmark, Compositional Visual Relations (CVR), to drive progress towards the development of more data-efficient learning algorithms. We take inspiration from fluidic intelligence and non-verbal reasoning tests and describe a novel method for creating compositions of abs
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