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Life sciences · Preprint
arXiv · September 9, 2026
Early or partial results. Treat as a signal, not a conclusion.
Vague2Detect is a hybrid software pipeline combining Sentence-BERT retrieval, YOLO-World image verification, and GPT-3.5-turbo fallback generation to improve object detection under ambiguous natural-language prompts. On a household scene benchmark, the system raises the baseline YOLO-World performance from 32% to 61% Vague Prompt Success Rate, reaching 85% with GPT augmentation, but results are unreviewed and generalizability beyond the tested benchmark is unknown.
Benchmark evaluation; system design and comparative performance assessment. Household scenes from custom images and Open Images V7 subset. Intervention: Vague2Detect hybrid pipeline: Sentence-BERT retrieval from structured Knowledge Base + YOLO-World verification + GPT-3.5-turbo candidate generation for out-of-KB prompts. Compared with: YOLO-World baseline object detector.
YOLO-World alone achieves 32% Vague Prompt Success Rate (VPSR) on household scene benchmark Vague2Detect improves VPSR to 61% with high precision With GPT-3.5-turbo fallback, VPSR reaches 85%
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This is an unreviewed technical report describing a proof-of-concept pipeline for object detection under ambiguous prompts, with no peer review or independent validation; the work is methodological and system-focused rather than a clinical or validated diagnostic study.
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Real-world detectors must often interpret functional or ambiguous prompts, yet conventional models such as YOLO remain restricted to fixed class lists. Even open-vocabulary models like YOLO-World frequently misalign vague language with the intended objects. Building on our prior work Commonsense-Guided Open-World Object Detection Using LLMs and Visual-Semantic Matching, we address YOLO-World's limitations in grounding task-driven queries. We propose Vague2Detect, a hybrid pipeline in which a fine-tuned Sentence-BERT retrieves candidates from a structured household Knowledge Base (KB), and YOLO-World verifies their presence in the image. For prompts outside the KB, a large language model (GPT-3.5-turbo) generates candidate descriptions, dynamically expanding the KB to cover novel concepts. On a benchmark of household scenes using custom images and an Open Images V7 subset, YOLO-World alone achieves only 32% Vague Prompt Success Rate (VPSR), the ability to map ambiguous queries to correct detections. In contrast, Vague2Detect improves performance to 61% VPSR with high precision, and up to 85% when augmented with GPT fallback.
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