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papersSEP 10 04:00 UTC

Vague2Detect addresses ambiguous prompts in knowledge-based open-world detection

A newly posted arXiv paper, cross-listed in computational linguistics and machine learning, presents Vague2Detect, a detection approach designed to work with vague or functional language prompts. The authors note that fixed-class detectors like YOLO and even open-vocabulary systems such as YOLO-World often misinterpret ambiguous wording and fail to match it to the intended objects. The proposed knowledge-based method aims to close this gap for real-world detection scenarios.

arXiv Vague2Detect YOLO YOLO-World open-world detection vague prompts

COVERAGE · 2 REPORTS · LINKS GO TO THE ORIGINAL OUTLETS

arXiv cs.LGVague2Detect: Handling Ambiguous Prompts in Knowledge-Based Open-World Detection ↗SEP 10 04:00 UTC

arXiv cs.CLVague2Detect: Handling Ambiguous Prompts in Knowledge-Based Open-World Detection ↗SEP 10 04:00 UTC