Grounded SAM: Assembling Open-World Models for Diverse Visual Tasks

We introduce Grounded SAM, which uses Grounding DINO as an open-set object detector to combine with the segment anything model (SAM). This integration enables the detection and segmentation of any regions based on arbitrary text inputs and opens a door to connecting various vision models. As shown i...

Full description

Saved in:
Bibliographic Details
Published in:arXiv.org 2024-01
Main Authors: Ren, Tianhe, Liu, Shilong, Zeng, Ailing, Lin, Jing, Li, Kunchang, Cao, He, Chen, Jiayu, Huang, Xinyu, Chen, Yukang, Feng, Yan, Zeng, Zhaoyang, Zhang, Hao, Li, Feng, Yang, Jie, Li, Hongyang, Jiang, Qing, Zhang, Lei
Format: Article
Language:English
Subjects:
Annotations
Benchmarks
Controllability
Human motion
Three dimensional motion
Vision
Visual tasks
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:We introduce Grounded SAM, which uses Grounding DINO as an open-set object detector to combine with the segment anything model (SAM). This integration enables the detection and segmentation of any regions based on arbitrary text inputs and opens a door to connecting various vision models. As shown in Fig.1, a wide range of vision tasks can be achieved by using the versatile Grounded SAM pipeline. For example, an automatic annotation pipeline based solely on input images can be realized by incorporating models such as BLIP and Recognize Anything. Additionally, incorporating Stable-Diffusion allows for controllable image editing, while the integration of OSX facilitates promptable 3D human motion analysis. Grounded SAM also shows superior performance on open-vocabulary benchmarks, achieving 48.7 mean AP on SegInW (Segmentation in the wild) zero-shot benchmark with the combination of Grounding DINO-Base and SAM-Huge models.
ISSN:2331-8422