Beyond Benchmarks: Using VLMs to Reveal Systematic Classification Failures Under Real World Conditions

2026-09-10

AI Summary

本文探索使用视觉语言模型加速分类模型验证和确认过程中的人工检查，提出了一种基于VLM的错误切片检测方法来识别系统性错误。

Abstract

Verification and validation (V&V) of classification models is crucial to enable a wide range of sensor processing applications. Currently, the V&V process relies on time-consuming manual inspection of erroneous samples to find meaningful patterns. This work explores the use of Vision Language Models (VLMs) to speed up this laborious process. VLMs are trained to embed images into a semantically meaningful vector representation, from which human-interpretable systematic errors can be distilled. Deploying such VLM-based methods in a defence context introduces two major challenges: (1) the defence domain is underrepresented in the training data of VLMs, and (2) surroundings and context are less diverse than for other domains. This study provides an initial assessment of the suitability of VLM-based methods for V&V of defence applications. We propose a VLM-based error slice detection (ESD) method that independently groups and labels systematic errors made by a classification model. We demonstrate that this method is able to identify operationally-relevant artificially added perturbations in a non-military dataset. In a military context, our method clusters and describes images based on their surroundings, but also exhibits overlap between cluster descriptions. We further investigate the difference in embedding variation between our military and non-military dataset, which remains a topic of interest. Although the results do not yet warrant fully automated V&V through VLM-based ESD, they show that VLMs could be used to accelerate V&V processes in the future.

Problem

• Verification and Validation
• Systematic Classification Failures
• Vision Language Models
• Error Slice Detection

Innovation

• Vision Language Models
• Error Slice Detection
• Systematic Classification Failures