arXiv Computer Vision October 7, 2026 By Eshika Khandelwal, Jingjing Pan, Mingfang Zhang, Quan Kong, Lorenzo Garattoni, Hilde Kuehne

FindIt: A Format-Informed Visual Detection Benchmark for Generalist Multimodal LLMs

Read the original on arXiv Computer Vision →

The paper introduces FindIt, the first comprehensive benchmark for evaluating the promptable localization abilities of generalist multimodal large language models (MLLMs). It covers four core task categories—object detection, referring expression detection, instance-level detection, and video-based detection—and provides a unified framework that standardizes inputs, enforces parsable bounding box outputs, and defines transparent evaluation protocols. Using this benchmark, the authors assess a range of open-source and proprietary MLLMs, revealing both their strengths and limitations, particularly their sensitivity to formatting constraints and difficulty generalizing to minor variations.

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