PixCC
A visual-stimulus benchmark dataset for evaluating associative creativity, in which the difficulty of image recognition and naming is increased by adding random visual noise.
Timeline (last 90 days)
The study uses two datasets: SilCC (which modulates naming difficulty through progressively complex abstract shape compositions) and PixCC (which increases recognition difficulty by adding random visual noise) to compare the associative creativity of humans and multimodal large models.
In the PixCC dataset, humans showed a strategy shift: from holistic semantic association to describing low-level visual attributes (such as color and texture), whereas MLLMs tended to adhere to semantic-level association.
All Facts (2)
The study uses two datasets: SilCC (which modulates naming difficulty through progressively complex abstract shape compositions) and PixCC (which increases recognition difficulty by adding random visual noise) to compare the associative creativity of humans and multimodal large models.
50%UnverifiedIn the PixCC dataset, humans showed a strategy shift: from holistic semantic association to describing low-level visual attributes (such as color and texture), whereas MLLMs tended to adhere to semantic-level association.
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