Prompt-Driven Image Analysis with Multimodal Generative AI: Detection, Segmentation, Inpainting, and Interpretation
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866915488456507392 |
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| author | Ahmad, Kaleem |
| author_facet | Ahmad, Kaleem |
| contents | Prompt-driven image analysis converts a single natural-language instruction into multiple steps: locate, segment, edit, and describe. We present a practical case study of a unified pipeline that combines open-vocabulary detection, promptable segmentation, text-conditioned inpainting, and vision-language description into a single workflow. The system works end to end from a single prompt, retains intermediate artifacts for transparent debugging (such as detections, masks, overlays, edited images, and before and after composites), and provides the same functionality through an interactive UI and a scriptable CLI for consistent, repeatable runs. We highlight integration choices that reduce brittleness, including threshold adjustments, mask inspection with light morphology, and resource-aware defaults. In a small, single-word prompt segment, detection and segmentation produced usable masks in over 90% of cases with an accuracy above 85% based on our criteria. On a high-end GPU, inpainting makes up 60 to 75% of total runtime under typical guidance and sampling settings, which highlights the need for careful tuning. The study offers implementation-guided advice on thresholds, mask tightness, and diffusion parameters, and details version pinning, artifact logging, and seed control to support replay. Our contribution is a transparent, reliable pattern for assembling modern vision and multimodal models behind a single prompt, with clear guardrails and operational practices that improve reliability in object replacement, scene augmentation, and removal. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_08489 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Prompt-Driven Image Analysis with Multimodal Generative AI: Detection, Segmentation, Inpainting, and Interpretation Ahmad, Kaleem Computer Vision and Pattern Recognition Artificial Intelligence Prompt-driven image analysis converts a single natural-language instruction into multiple steps: locate, segment, edit, and describe. We present a practical case study of a unified pipeline that combines open-vocabulary detection, promptable segmentation, text-conditioned inpainting, and vision-language description into a single workflow. The system works end to end from a single prompt, retains intermediate artifacts for transparent debugging (such as detections, masks, overlays, edited images, and before and after composites), and provides the same functionality through an interactive UI and a scriptable CLI for consistent, repeatable runs. We highlight integration choices that reduce brittleness, including threshold adjustments, mask inspection with light morphology, and resource-aware defaults. In a small, single-word prompt segment, detection and segmentation produced usable masks in over 90% of cases with an accuracy above 85% based on our criteria. On a high-end GPU, inpainting makes up 60 to 75% of total runtime under typical guidance and sampling settings, which highlights the need for careful tuning. The study offers implementation-guided advice on thresholds, mask tightness, and diffusion parameters, and details version pinning, artifact logging, and seed control to support replay. Our contribution is a transparent, reliable pattern for assembling modern vision and multimodal models behind a single prompt, with clear guardrails and operational practices that improve reliability in object replacement, scene augmentation, and removal. |
| title | Prompt-Driven Image Analysis with Multimodal Generative AI: Detection, Segmentation, Inpainting, and Interpretation |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2509.08489 |