Advancing Generalization Across a Variety of Abstract Visual Reasoning Tasks
This addresses generalization issues in AI models for visual reasoning, though it appears incremental as it builds on existing architectures.
The paper tackles the challenge of out-of-distribution generalization in abstract visual reasoning tasks, such as Raven's Progressive Matrices, by proposing the PoNG model, which outperforms existing methods in several settings.
The abstract visual reasoning (AVR) domain presents a diverse suite of analogy-based tasks devoted to studying model generalization. Recent years have brought dynamic progress in the field, particularly in i.i.d. scenarios, in which models are trained and evaluated on the same data distributions. Nevertheless, o.o.d. setups that assess model generalization to new test distributions remain challenging even for the most recent models. To advance generalization in AVR tasks, we present the Pathways of Normalized Group Convolution model (PoNG), a novel neural architecture that features group convolution, normalization, and a parallel design. We consider a wide set of AVR benchmarks, including Raven's Progressive Matrices and visual analogy problems with both synthetic and real-world images. The experiments demonstrate strong generalization capabilities of the proposed model, which in several settings outperforms the existing literature methods.