Abstract
Visual reasoning is critical for a wide range of computer vision tasks that go beyond surface-level object detection and classification. Despite notable advances in relational, symbolic, temporal, causal, and commonsense reasoning, existing surveys often address these directions in isolation, lacking a unified analysis and comparison across reasoning types, methodologies, and evaluation protocols. This survey aims to address this gap by categorizing visual reasoning into five major types (relational, symbolic, temporal, causal, and commonsense) and systematically examining their implementation through architectures such as graph-based models, memory networks, attention mechanisms, and neuro-symbolic systems. We review evaluation protocols designed to assess functional correctness, structural consistency, and causal validity, and critically analyze their limitations in terms of generalizability, reproducibility, and explanatory power. Beyond evaluation, we identify key open challenges in visual reasoning, including scalability to complex scenes, deeper integration of symbolic and neural paradigms, the lack of comprehensive benchmark datasets, and reasoning under weak supervision. Finally, we outline a forward-looking research agenda for next-generation vision systems, emphasizing that bridging perception and reasoning is essential for building transparent, trustworthy, and cross-domain adaptive AI systems, particularly in critical domains such as autonomous driving and medical diagnostics.
Abstract (translated)
视觉推理对于超越表面级对象检测和分类的广泛计算机视觉任务至关重要。尽管在关系、符号、时间、因果以及常识推理方面取得了显著进展，现有的综述往往孤立地探讨这些方向，缺乏对不同推理类型、方法论及评估协议的统一分析与比较。本次调查旨在通过将视觉推理归类为五大主要类型（关系型、符号型、时序型、因果型和常识型），并系统性地审查其通过图模型、内存网络、注意力机制以及神经-符号系统的实现来填补这一空白。我们回顾了用于评估功能正确性、结构一致性及因果有效性的评价协议，并批判性分析了它们在泛化能力、可重复性和解释力方面的局限性。除了评估之外，我们还识别出视觉推理领域的关键开放挑战，包括对复杂场景的扩展性问题、符号与神经范式更深层次的整合需求、缺乏全面基准数据集以及弱监督下的推理难题。最后，我们为下一代视觉系统展望了一个前瞻性的研究议程，强调了将感知和推理相融合对于构建透明、可信且跨领域适应性强的人工智能系统的必要性，尤其是在自动驾驶及医学诊断等关键领域中尤为如此。
URL
https://arxiv.org/abs/2508.10523
