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We present F-VLM, a simple open-vocabulary object detection method built uponFrozen Vision and Language Models. F-VLM simplifies the current multi-stagetraining pipeline by eliminating the need for knowledge distillation ordetection-tailored pretraining. Surprisingly, we observe that a frozen VLM: 1)retains the locality-sensitive features necessary for detection, and 2) is astrong region classifier. We finetune only the detector head and combine thedetector and VLM outputs for each region at inference time. F-VLM showscompelling scaling behavior and achieves +6.5 mask AP improvement over theprevious state of the art on novel categories of LVIS open-vocabulary detectionbenchmark. In addition, we demonstrate very competitive results on COCOopen-vocabulary detection benchmark and cross-dataset transfer detection, inaddition to significant training speed-up and compute savings. Code will bereleased at the https://sites.google.com/view/f-vlm/home

F-VLM: Open-Vocabulary Object Detection upon Frozen Vision and Language Models