Fast or Slow? Integrating Fast Intuition and Deliberate Thinking for Enhancing Visual Question Answering
Songtao Jiang, Chenyi Zhou, Yan Zhang, Yeying Jin, Zuozhu Liu · 2025
Multimodal large language models (MLLMs) still struggle with complex reasoning tasks in Visual Question Answering (VQA).While current methods have advanced by incorporating visual prompts, our study uncovers critical limitations: these approaches indiscriminately annotate all detected objects for every visual question, generating excessive visual markers that degrade task performance.This issue stems primarily from a lack of focus on key visual elements, raising two important questions: Are all objects equally important, and do all questions require visual prompts?Motivated by Dual Process Theory, which distinguishes between instinctive and deliberate cognitive modes in human reasoning, we propose FOCUS, a plug-and-play approach that dynamically adapts to the complexity of questions, combining fast intuitive judgments with deliberate analytical reasoning to enhance the vision-language reasoning capability of the MLLM.For straightforward questions, FOCUS supports efficient zero-shot reasoning.For more complex tasks, it employs the conceptualizing before observation strategy to highlight critical elements.Extensive experiments on four benchmarks-ScienceQA, TextQA, VizWiz, and MME-demonstrate that FOCUS consistently improves the performance of both open-source and black-box MLLMs, achieving significant gains across all datasets.Ablation studies further validate the importance of combining diverse cognitive strategies with refined visual information for superior performance.Code will be released.