Cognitive Paradigms for Evaluating VLMs on Visual Reasoning Task

Vision-Language Models (VLMs) excel in complex visual tasks, surpassing human benchmarks. Three strategies—holistic analysis, deductive rule learning, and componential analysis—enhance reasoning capabilities. Challenges include handling synthetic images, making fine-grained distinctions, and interpreting nuanced contextual information. Ablation studies highlight the need for model advancements in robustness and generalization, emphasizing structured reasoning's transformative potential.