Evaluating the Robustness of Open-Source Vision-Language Models to Domain Shift in Object Captioning

• URL: http://arxiv.org/abs/2506.19579v2
• Date: Tue, 16 Sep 2025 15:12:16 GMT
• Title: Evaluating the Robustness of Open-Source Vision-Language Models to Domain Shift in Object Captioning
• Authors: Federico Tavella, Amber Drinkwater, Angelo Cangelosi,
• Abstract summary: Vision-Language Models (VLMs) have emerged as powerful tools for generating textual descriptions from visual data.<n>This paper presents a systematic evaluation of VLM performance on a single-view object captioning task.<n>We compare captioning accuracy across two distinct object sets: a collection of multi-material, real-world tools and a set of single-material, 3D-printed items.
• Score: 4.180203626942459
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Vision-Language Models (VLMs) have emerged as powerful tools for generating textual descriptions from visual data. While these models excel on web-scale datasets, their robustness to the domain shifts inherent in many real-world applications remains under-explored. This paper presents a systematic evaluation of VLM performance on a single-view object captioning task when faced with a controlled, physical domain shift. We compare captioning accuracy across two distinct object sets: a collection of multi-material, real-world tools and a set of single-material, 3D-printed items. The 3D-printed set introduces a significant domain shift in texture and material properties, challenging the models' generalization capabilities. Our quantitative results demonstrate that all tested VLMs show a marked performance degradation when describing the 3D-printed objects compared to the real-world tools. This underscores a critical limitation in the ability of current models to generalize beyond surface-level features and highlights the need for more robust architectures for real-world signal processing applications.

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