Does Data Scaling Lead to Visual Compositional Generalization?
Authors: name: Arnas Uselis, name: Andrea Dittadi, name: Seong Joon Oh

Compositional understanding is crucial for human intelligence, yet it remains
unclear whether contemporary vision models exhibit it. The dominant machine
learning paradigm is built on the premise that scaling data and model sizes
will improve out-of-distribution performance, including compositional
generalization. We test this premise through controlled experiments that
systematically vary data scale, concept diversity, and combination coverage. We
find that compositional generalization is driven by data diversity, not mere
data scale. Increased combinatorial coverage forces models to discover a
linearly factored representational structure, where concepts decompose into
additive components. We prove this structure is key to efficiency, enabling
perfect generalization from few observed combinations. Evaluating pretrained
models (DINO, CLIP), we find above-random yet imperfect performance, suggesting
partial presence of this structure. Our work motivates stronger emphasis on
constructing diverse datasets for compositional generalization, and considering
the importance of representational structure that enables efficient
compositional learning. Code available at
https://github.com/oshapio/visual-compositional-generalization.