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In-Person Poster presentation / poster accept

Open-Vocabulary Object Detection upon Frozen Vision and Language Models

Weicheng Kuo · Yin Cui · Xiuye Gu · AJ Piergiovanni · Anelia Angelova

MH1-2-3-4 #50

Keywords: [ vision and language ] [ object detection ] [ open-vocabulary recognition ] [ Applications ]


Abstract:

We present F-VLM, a simple open-vocabulary object detection method built uponFrozenVision andLanguageModels. 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 theart 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.

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