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

• URL: http://arxiv.org/abs/2209.15639v1
• Date: Fri, 30 Sep 2022 17:59:52 GMT
• Title: F-VLM: Open-Vocabulary Object Detection upon Frozen Vision and Language Models
• Authors: Weicheng Kuo, Yin Cui, Xiuye Gu, AJ Piergiovanni, Anelia Angelova
• Abstract summary: 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.
• Score: 54.21757555804668
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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