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Reasoning in Computer Vision: Taxonomy, Models, Tasks, and Methodologies

Ayushman Sarkar, Mohd Yamani Idna Idris, Zhenyu Yu

TL;DR

The paper advocates a unified taxonomy of five visual reasoning types—relational, symbolic, temporal, causal, and commonsense—and surveys architectural families such as graph-based models, memory-enabled systems, attention-based networks, and neuro-symbolic pipelines. It analyzes evaluation protocols across functional, structural, and causal dimensions, highlighting limitations in generalizability, reproducibility, and explanatory power, and emphasizes the need for richer benchmarks and weakly supervised learning to scale reasoning to real-world scenarios. The authors argue for multi-paradigm hybrids, grounding symbolic and causal insights in scalable neural architectures, and call for standardized, cross-domain evaluation to enable transparent, trustworthy AI in critical domains like autonomous driving and healthcare. A forward-looking agenda stresses scalable, modular, and knowledge-grounded systems, along with cross-task benchmark development and adaptive evaluation pipelines that better capture reasoning fidelity and robustness in open-world settings.

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.

Reasoning in Computer Vision: Taxonomy, Models, Tasks, and Methodologies

TL;DR

The paper advocates a unified taxonomy of five visual reasoning types—relational, symbolic, temporal, causal, and commonsense—and surveys architectural families such as graph-based models, memory-enabled systems, attention-based networks, and neuro-symbolic pipelines. It analyzes evaluation protocols across functional, structural, and causal dimensions, highlighting limitations in generalizability, reproducibility, and explanatory power, and emphasizes the need for richer benchmarks and weakly supervised learning to scale reasoning to real-world scenarios. The authors argue for multi-paradigm hybrids, grounding symbolic and causal insights in scalable neural architectures, and call for standardized, cross-domain evaluation to enable transparent, trustworthy AI in critical domains like autonomous driving and healthcare. A forward-looking agenda stresses scalable, modular, and knowledge-grounded systems, along with cross-task benchmark development and adaptive evaluation pipelines that better capture reasoning fidelity and robustness in open-world settings.

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.

Paper Structure

This paper contains 34 sections, 2 theorems, 7 equations, 5 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Foundations
  3. Definition
  4. Representations
  5. Reasoning-Centric Tasks
  6. Relational Reasoning
  7. Compositional Visual Question Answering (VQA)
  8. Commonsense and Intent Reasoning
  9. Causal and Counterfactual Reasoning
  10. Temporal Reasoning
  11. Methodologies
  12. Symbolic and Neuro-Symbolic
  13. Relational and Graph-Based Neural Reasoning
  14. Causal Reasoning in Vision
  15. Modular and Sequential Reasoning Architectures
  16. ...and 19 more sections

Key Result

Theorem 1

Example theorem text. Example theorem text. Example theorem text. Example theorem text. Example theorem text. Example theorem text. Example theorem text. Example theorem text. Example theorem text. Example theorem text. Example theorem text.

Figures (5)

  • Figure 1: Survey structure showing reasoning types, representative methodologies, and evaluation dimensions in visual reasoning.
  • Figure 2: Neuro-symbolic reasoning pipeline combining visual parsing and symbolic execution.
  • Figure 3: Relational reasoning over a scene graph using object-object relationships.
  • Figure 4: Causal graph based on structural causal model (SCM) modeling interactions and interventions in visual tasks.
  • Figure 5: Causal DAG representing visual dependencies: e.g., Banana$\rightarrow$Step$\rightarrow$Fall.

Theorems & Definitions (7)

  • Theorem 1: Theorem subhead
  • Proposition 2
  • Example 1
  • Remark 1
  • Definition 1: Definition sub head
  • proof
  • proof : Proof of Theorem \ref{['thm1']}