Chain-of-Procedure: Hierarchical Visual-Language Reasoning for Procedural QA

TL;DR

This paper introduces Chain-of-Procedure, a hierarchical reasoning framework that enhances visual-language models for procedural question answering by improving retrieval and step decomposition, leading to significant performance gains.

Contribution

The paper presents a novel hierarchical reasoning framework, Chain-of-Procedure, addressing key limitations in current VLMs for visual procedural reasoning tasks.

Findings

Up to 13% absolute improvement over baselines with CoP.

Identified critical limitations in cross-modal retrieval and step granularity.

Proposed ProcedureVQA benchmark for evaluating VP-QA models.

Abstract

Recent advances in vision-language models (VLMs) have achieved impressive results on standard image-text tasks, yet their potential for visual procedure question answering (VP-QA) remains largely unexplored. VP-QA presents unique challenges where users query next-step actions by uploading images for intermediate states of complex procedures. To systematically evaluate VLMs on this practical task, we propose ProcedureVQA, a novel multimodal benchmark specifically designed for visual procedural reasoning. Through comprehensive analysis, we identify two critical limitations in current VLMs: inadequate cross-modal retrieval of structured procedures given visual states, and misalignment between image sequence granularity and textual step decomposition. To address these issues, we present Chain-of-Procedure (CoP), a hierarchical reasoning framework that first retrieves relevant instructions using visual cues, then performs step refinement through semantic decomposition, and finally generates the next step. Experiments across six VLMs demonstrate CoP's effectiveness, achieving up to 13% absolute improvement over standard baselines.