InspectVLM: Unified in Theory, Unreliable in Practice

2025-08-03

AI Summary

This work investigates the feasibility and robustness of unified vision-language models (VLMs) as replacements for task-specific models in industrial inspection. To address diverse requirements—including image classification, object detection, and keypoint localization—it proposes a language-interface-based unified paradigm, introduces InspectMM—the first large-scale multimodal industrial inspection dataset—and performs instruction tuning on Florence-2. Experiments show strong performance on classification and structured keypoint tasks, but limited robustness on fine-grained detection, high sensitivity to prompt engineering, and weaker visual grounding compared to specialized architectures like ResNet. The core contribution lies in empirically exposing the fundamental tension between *linguistic unification* and *visual reliability* in VLMs for industrial applications, thereby establishing an empirical benchmark and identifying concrete directions for advancing VLMs toward high-precision industrial deployment.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Computer Vision: Language and Vision
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Unified vision-language models (VLMs) promise to streamline computer vision pipelines by reframing multiple visual tasks such as classification, detection, and keypoint localization within a single language-driven interface. This architecture is particularly appealing in industrial inspection, where managing disjoint task-specific models introduces complexity, inefficiency, and maintenance overhead. In this paper, we critically evaluate the viability of this unified paradigm using InspectVLM, a Florence-2-based VLM trained on InspectMM, our new large-scale multimodal, multitask inspection dataset. While InspectVLM performs competitively on image-level classification and structured keypoint tasks, we find that it fails to match traditional ResNet-based models in core inspection metrics. Notably, the model exhibits brittle behavior under low prompt variability, produces degenerate outputs for fine-grained object detection, and frequently defaults to memorized language responses regardless of visual input. Our findings suggest that while language-driven unification offers conceptual elegance, current VLMs lack the visual grounding and robustness necessary for deployment in precision critical industrial inspections.

Problem

• Evaluating unified VLMs' reliability in industrial inspection tasks
• Assessing performance gaps between VLMs and traditional models
• Identifying brittleness in prompt variability and visual grounding

Innovation

• Unified vision-language model for multiple tasks
• Trained on large-scale multimodal inspection dataset
• Evaluated performance against traditional ResNet models