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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2502.15389 |
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| _version_ | 1866909503597838336 |
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| author | Tomita, Masayo Hayashi, Katsuhiko Kaneko, Tomoyuki |
| author_facet | Tomita, Masayo Hayashi, Katsuhiko Kaneko, Tomoyuki |
| contents | Vision-Language Models (VLMs) occasionally generate outputs that contradict input images, constraining their reliability in real-world applications. While visual prompting is reported to suppress hallucinations by augmenting prompts with relevant area inside an image, the effectiveness in terms of the area remains uncertain. This study analyzes success and failure cases of Attention-driven visual prompting in object hallucination, revealing that preserving background context is crucial for mitigating object hallucination. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_15389 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | The Role of Background Information in Reducing Object Hallucination in Vision-Language Models: Insights from Cutoff API Prompting Tomita, Masayo Hayashi, Katsuhiko Kaneko, Tomoyuki Computer Vision and Pattern Recognition Vision-Language Models (VLMs) occasionally generate outputs that contradict input images, constraining their reliability in real-world applications. While visual prompting is reported to suppress hallucinations by augmenting prompts with relevant area inside an image, the effectiveness in terms of the area remains uncertain. This study analyzes success and failure cases of Attention-driven visual prompting in object hallucination, revealing that preserving background context is crucial for mitigating object hallucination. |
| title | The Role of Background Information in Reducing Object Hallucination in Vision-Language Models: Insights from Cutoff API Prompting |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2502.15389 |