  Visual abstract reasoning problems pose significant challenges to the
perception and cognition abilities of artificial intelligence algorithms,
demanding deeper pattern recognition and inductive reasoning beyond mere
identification of explicit image features. Research advancements in this field
often provide insights and technical support for other similar domains. In this
study, we introduce PMoC, a deep-learning-based probabilistic model, achieving
high reasoning accuracy in the Bongard-Logo, which stands as one of the most
challenging clustering reasoning tasks. PMoC is a novel approach for
constructing probabilistic models based on deep learning, which is distinctly
different from previous techniques. PMoC revitalizes the probabilistic
approach, which has been relatively weak in visual abstract reasoning. As a
bonus, we also designed Pose-Transformer for complex visual abstract reasoning
tasks. Inspired by capsule networks, it focuses on positional relationships in
image data, boosting accuracy when combined with PMoC. Our Pose-Transformer
effectively addresses reasoning difficulties associated with changes in the
position of entities, outperforming previous models on RAVEN dataset, and the
PGM dataset. RAVEN and PGM represent two significant progressive pattern
reasoning problems. Finally, considering the deployment difficulties of
Pose-Transformer, we introduced Straw-Pose-Transformer, a lightweight version.
This study contributes to enhancing the capabilities of artificial intelligence
in abstract reasoning, cognitive pattern, and probabilistic modeling of complex
systems.
