Vision-Language Model (VLM) have gained widespread adoption in Open-Vocabulary (OV) object detection and segmentation tasks. Despite they have shown promise on OV-related tasks, their effectiveness in conventional vision tasks has thus far been unevaluated. In this work, we present the systematic review of VLM-based detection and segmentation, view VLM as the foundational model and conduct comprehensive evaluations across multiple downstream tasks for the first time: 1) The evaluation spans eight detection scenarios (closed-set detection, domain adaptation, crowded objects, etc.) and eight segmentation scenarios (few-shot, open-world, small object, etc.), revealing distinct performance advantages and limitations of various VLM architectures across tasks. 2) As for detection tasks, we evaluate VLMs under three finetuning granularities: \textit{zero prediction}, \textit{visual fine-tuning}, and \textit{text prompt}, and further analyze how different finetuning strategies impact performance under varied task. 3) Based on empirical findings, we provide in-depth analysis of the correlations between task characteristics, model architectures, and training methodologies, offering insights for future VLM design. 4) We believe that this work shall be valuable to the pattern recognition experts working in the fields of computer vision, multimodal learning, and vision foundation models by introducing them to the problem, and familiarizing them with the current status of the progress while providing promising directions for future research. A project associated with this review and evaluation has been created atthis https URL.
View on arXiv@article{feng2025_2504.09480,
title={ Vision-Language Model for Object Detection and Segmentation: A Review and Evaluation },
author={ Yongchao Feng and Yajie Liu and Shuai Yang and Wenrui Cai and Jinqing Zhang and Qiqi Zhan and Ziyue Huang and Hongxi Yan and Qiao Wan and Chenguang Liu and Junzhe Wang and Jiahui Lv and Ziqi Liu and Tengyuan Shi and Qingjie Liu and Yunhong Wang },
journal={arXiv preprint arXiv:2504.09480},
year={ 2025 }
}