Prompt Sensitivity in Vision-Language Grounding: How Small Changes in Wording Affect Object Detection
Vision-language models enable open-vocabulary object grounding through natural language queries, under the implicit assumption that semantically equivalent descriptions yield consistent outputs. We examine this assumption using a controlled pipeline combining DETR for object proposals with CLIP for language-conditioned selection on 263 COCO val2017 images. We find that overlapping prompts such as "a person," "a human," and "a pedestrian" frequently select different instances, with mean instability of 2.11 distinct selections across six prompts. PCA analysis shows this variability is structured and directional, not random. Prompt ensembling does not improve quality and often shifts selecti...
| Authors | Dawar Jyoti Deka, Amit Sethi, Syed Mohammad Ali |
|---|---|
| Primary category | cs.CV |
| Published | 18 April 2026 |
| Last updated at source | 18 April 2026 |
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