见森林与见树木：面向知识驱动视觉问答的协同推理框架
See the Forest and the Trees: A Synergistic Reasoning Framework for Knowledge-Based Visual Question Answering
AI总结：
针对多模态大模型在知识驱动视觉问答中依赖单一维度证据的瓶颈，提出协同推理框架Synergos-VQA，在推理时融合整体、结构和因果证据，在多个基准上取得新SOTA并具备即插即用能力。
摘要：
多模态大语言模型（MLLMs）推动了知识驱动视觉问答（KBVQA）的发展，但其推理从根本上受限于对单一维度证据的依赖。这种“只见树木，不见森林”的方法阻碍了鲁棒的多维理解。受“见森林与见树木”原则的启发，我们提出了Synergos-VQA，一种新颖的协同推理框架。其核心在于，Synergos-VQA在推理时同时生成并融合三种互补的证据流：（1）整体证据，用于感知整个场景（“森林”）；（2）结构证据，通过原型驱动模块识别关键对象（“树木”）；（3）因果证据，通过反事实探针确保推理具有鲁棒的视觉基础。通过协同融合这种多维证据，我们的框架实现了更全面且可靠的推理过程。大量实验表明，Synergos-VQA在包括OK-VQA和A-OKVQA在内的三个挑战性基准上决定性地确立了新的最先进水平。此外，该方法展现出强大的即插即用能力，显著提升了多种开源MLLMs，证明了卓越的方法设计能够超越单纯的模型规模。
英文摘要：
Multimodal Large Language Models (MLLMs) have pushed the frontiers of Knowledge-Based Visual Question Answering (KBVQA), yet their reasoning is fundamentally bottlenecked by a reliance on uni-dimensional evidence. This "seeing only the trees, but not the forest" approach prevents robust, multi-faceted understanding. Inspired by the principle of seeing both the forest and trees, we propose Synergos-VQA, a novel synergistic reasoning framework. At its core, Synergos-VQA concurrently generates and fuses three complementary evidence streams at inference time: (1) Holistic Evidence to perceive the entire scene (the "forest"), (2) Structural Evidence from a prototype-driven module to identify key objects (the "trees"), and (3) Causal Evidence from a counterfactual probe to ensure the reasoning is robustly grounded. By synergistically fusing this multi-faceted evidence, our framework achieves a more comprehensive and reliable reasoning process. Extensive experiments show that Synergos-VQA decisively establishes a new state-of-the-art on three challenging benchmarks, including OK-VQA and A-OKVQA. Furthermore, our approach demonstrates strong plug-and-play capabilities, significantly boosting various open-source MLLMs and proving that superior methodological design can outperform sheer model scale.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
