🤖 AI Summary
This work addresses the challenge of unifying object localization, segmentation, editing, and description into a single, transparent, reliable, and reproducible image analysis pipeline driven by one natural language instruction.
Method: We propose an end-to-end, prompt-driven, multi-stage framework integrating open-vocabulary detection, promptable segmentation, text-conditioned image inpainting, and vision-language description. A transparent multimodal model integration paradigm is introduced, incorporating intermediate result persistence, adaptive threshold tuning, lightweight morphological mask post-processing, and dependency version locking. The system supports both CLI and UI modes, with seed control, comprehensive logging, and parameter serialization.
Contribution/Results: Experiments show >90% usability and >85% accuracy for mask generation under word-level prompting; inpainting accounts for 60–75% of total high-end GPU time, validating efficacy while highlighting optimization opportunities.
📝 Abstract
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.