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

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arxiv 2209.15639 v2 pith:KONG6QI2 submitted 2022-09-30 cs.CV

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

Weicheng Kuo , Yin Cui , Xiuye Gu , AJ Piergiovanni , Anelia Angelova This is my paper
classification cs.CV
keywords detectionf-vlmopen-vocabularyfrozenadditionbenchmarkdetectorlanguage
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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We present F-VLM, a simple open-vocabulary object detection method built upon Frozen Vision and Language Models. F-VLM simplifies the current multi-stage training pipeline by eliminating the need for knowledge distillation or detection-tailored pretraining. Surprisingly, we observe that a frozen VLM: 1) retains the locality-sensitive features necessary for detection, and 2) is a strong region classifier. We finetune only the detector head and combine the detector and VLM outputs for each region at inference time. F-VLM shows compelling scaling behavior and achieves +6.5 mask AP improvement over the previous state of the art on novel categories of LVIS open-vocabulary detection benchmark. In addition, we demonstrate very competitive results on COCO open-vocabulary detection benchmark and cross-dataset transfer detection, in addition to significant training speed-up and compute savings. Code will be released at the https://sites.google.com/view/f-vlm/home

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Forward citations

Cited by 9 Pith papers

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