The recent Segment Anything Model (SAM) has emerged as a new paradigmatic
vision foundation model, showcasing potent zero-shot generalization and
flexible prompting. Despite SAM finding applications and adaptations in various
domains, its primary limitation lies in the inability to grasp object
semantics. In this paper, we present Sambor to seamlessly integrate SAM with
the open-vocabulary object detector in an end-to-end framework. While retaining
all the remarkable capabilities inherent to SAM, we enhance it with the
capacity to detect arbitrary objects based on human inputs like category names
or reference expressions. To accomplish this, we introduce a novel SideFormer
module that extracts SAM features to facilitate zero-shot object localization
and inject comprehensive semantic information for open-vocabulary recognition.
In addition, we devise an open-set region proposal network (Open-set RPN),
enabling the detector to acquire the open-set proposals generated by SAM.
Sambor demonstrates superior zero-shot performance across benchmarks, including
COCO and LVIS, proving highly competitive against previous SoTA methods. We
aspire for this work to serve as a meaningful endeavor in endowing SAM to
recognize diverse object categories and advancing open-vocabulary learning with
the support of vision foundation models.

本研究旨在无缝地将 Segment Anything Model (SAM) 与开放词汇目标检测器集成在一起，引入了 SideFormer 模块和开放区域建议网络 (Open-set RPN) 等创新方法，以提升 SAM 在检测任意对象和开放词汇识别方面的性能。Sambor 在 COCO 和 LVIS 等基准测试中展现出卓越的零样本性能，并与之前的最先进方法竞争力十足，旨在为 SAM 赋予识别多样化对象类别和促进视觉基础模型的开放词汇学习提供有意义的努力。