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arXiv Computation and Language By Ibrohimjon Muminov (Dongguk University, Seoul, South Korea), Jihie Kim (Dongguk University, Seoul, South Korea)

Vague2Detect: Handling Ambiguous Prompts in Knowledge-Based Open-World Detection

Read the original on arXiv Computation and Language →

Vague2Detect is a hybrid pipeline that improves object detection for ambiguous prompts by combining a fine‑tuned Sentence‑BERT to retrieve candidates from a structured household knowledge base, YOLO‑World to verify their presence in images, and a GPT‑3.5‑turbo fallback to generate new candidate descriptions when prompts fall outside the knowledge base. On a benchmark of household scenes, Vague2Detect raises the vague prompt success rate from 32% (YOLO‑World alone) to 61% with high precision, and up to 85% when the GPT fallback is used.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

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