Explain Before You Answer: A Survey on Compositional Visual Reasoning
Compositional visual reasoning has emerged as a key research frontier in
multimodal AI, aiming to endow machines with the human-like ability to
decompose visual scenes, ground intermediate concepts, and perform multi-step
logical inference. While early surveys focus on monolithic vision-language
models or general multimodal reasoning, a dedicated synthesis of the rapidly
expanding compositional visual reasoning literature is still missing. We fill
this gap with a comprehensive survey spanning 2023 to 2025 that systematically
reviews 260+ papers from top venues (CVPR, ICCV, NeurIPS, ICML, ACL, etc.). We
first formalize core definitions and describe why compositional approaches
offer advantages in cognitive alignment, semantic fidelity, robustness,
interpretability, and data efficiency. Next, we trace a five-stage paradigm
shift: from prompt-enhanced language-centric pipelines, through tool-enhanced
LLMs and tool-enhanced VLMs, to recently minted chain-of-thought reasoning and
unified agentic VLMs, highlighting their architectural designs, strengths, and
limitations. We then catalog 60+ benchmarks and corresponding metrics that
probe compositional visual reasoning along dimensions such as grounding
accuracy, chain-of-thought faithfulness, and high-resolution perception.
Drawing on these analyses, we distill key insights, identify open challenges
(e.g., limitations of LLM-based reasoning, hallucination, a bias toward
deductive reasoning, scalable supervision, tool integration, and benchmark
limitations), and outline future directions, including world-model integration,
human-AI collaborative reasoning, and richer evaluation protocols. By offering
a unified taxonomy, historical roadmap, and critical outlook, this survey aims
to serve as a foundational reference and inspire the next generation of
compositional visual reasoning research.
68ad3dec86b21a0e2e358e46
Fucai Ke
claimed_verified
2025-09-04T08:47:55.130Z
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/avatars/d2a637ab50f11473aca17a0d82707ba3.svg
Fucai Ke
user
Pokerme
68ad3dec86b21a0e2e358e47
Joy Hsu
68ad3dec86b21a0e2e358e48
Zhixi Cai
claimed_verified
2025-08-26T09:49:27.045Z
633de44a797cf1b030553d64
https://cdn-avatars.huggingface.co/v1/production/uploads/633de44a797cf1b030553d64/uvrAP6uYrKnpRxdcK8RKR.png
ControlNet
user
ControlNet
68ad3dec86b21a0e2e358e49
Zixian Ma
68ad3dec86b21a0e2e358e4a
Xin Zheng
68ad3dec86b21a0e2e358e4b
Xindi Wu
68ad3dec86b21a0e2e358e4c
Sukai Huang
68ad3dec86b21a0e2e358e4d
Weiqing Wang
68ad3dec86b21a0e2e358e4e
Pari Delir Haghighi
68ad3dec86b21a0e2e358e4f
Gholamreza Haffari
68ad3dec86b21a0e2e358e50
Ranjay Krishna
68ad3dec86b21a0e2e358e51
Jiajun Wu
68ad3dec86b21a0e2e358e52
Hamid Rezatofighi
Explain Before You Answer: A Survey on Compositional Visual Reasoning
Compositional visual reasoning has emerged as a key research frontier in
multimodal AI, aiming to endow machines with the human-like ability to
decompose visual scenes, ground intermediate concepts, and perform multi-step
logical inference. While early surveys focus on monolithic vision-language
models or general multimodal reasoning, a dedicated synthesis of the rapidly
expanding compositional visual reasoning literature is still missing. We fill
this gap with a comprehensive survey spanning 2023 to 2025 that systematically
reviews 260+ papers from top venues (CVPR, ICCV, NeurIPS, ICML, ACL, etc.). We
first formalize core definitions and describe why compositional approaches
offer advantages in cognitive alignment, semantic fidelity, robustness,
interpretability, and data efficiency. Next, we trace a five-stage paradigm
shift: from prompt-enhanced language-centric pipelines, through tool-enhanced
LLMs and tool-enhanced VLMs, to recently minted chain-of-thought reasoning and
unified agentic VLMs, highlighting their architectural designs, strengths, and
limitations. We then catalog 60+ benchmarks and corresponding metrics that
probe compositional visual reasoning along dimensions such as grounding
accuracy, chain-of-thought faithfulness, and high-resolution perception.
Drawing on these analyses, we distill key insights, identify open challenges
(e.g., limitations of LLM-based reasoning, hallucination, a bias toward
deductive reasoning, scalable supervision, tool integration, and benchmark
limitations), and outline future directions, including world-model integration,
human-AI collaborative reasoning, and richer evaluation protocols. By offering
a unified taxonomy, historical roadmap, and critical outlook, this survey aims
to serve as a foundational reference and inspire the next generation of
compositional visual reasoning research.
A comprehensive survey of compositional visual reasoning from 2023 to 2025 reviews advancements in multimodal AI, highlighting architectural designs, benchmarks, and future directions.
compositional visual reasoning multimodal AI cognitive alignment semantic fidelity robustness interpretability data efficiency prompt-enhanced pipelines tool-enhanced LLMs tool-enhanced VLMs chain-of-thought reasoning unified agentic VLMs grounding accuracy chain-of-thought faithfulness high-resolution perception LLM-based reasoning hallucination deductive reasoning scalable supervision tool integration benchmark limitations world-model integration human-AI collaborative reasoning richer evaluation protocols