Papers
arxiv:2406.20076

EVF-SAM: Early Vision-Language Fusion for Text-Prompted Segment Anything Model

Published on Jun 28
Β· Submitted by wondervictor on Jul 1
Authors:
Rui Hu ,
,
,
,
,

Abstract

Segment Anything Model (SAM) has attracted widespread attention for its superior interactive segmentation capabilities with visual prompts while lacking further exploration of text prompts. In this paper, we empirically investigate what text prompt encoders (e.g., CLIP or LLM) are good for adapting SAM for referring expression segmentation and introduce the Early Vision-language Fusion-based SAM (EVF-SAM). EVF-SAM is a simple yet effective referring segmentation method which exploits multimodal prompts (i.e., image and text) and comprises a pre-trained vision-language model to generate referring prompts and a SAM model for segmentation. Surprisingly, we observe that: (1) multimodal prompts and (2) vision-language models with early fusion (e.g., BEIT-3) are beneficial for prompting SAM for accurate referring segmentation. Our experiments show that the proposed EVF-SAM based on BEIT-3 can obtain state-of-the-art performance on RefCOCO/+/g for referring expression segmentation and demonstrate the superiority of prompting SAM with early vision-language fusion. In addition, the proposed EVF-SAM with 1.32B parameters achieves remarkably higher performance while reducing nearly 82% of parameters compared to previous SAM methods based on large multimodal models.

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Paper author Paper submitter

This paper explores the effective ways for prompting SAM with texts.

Congrats @wondervictor ! Are you planning on releasing any artifacts (models, datasets, demos) on the hub?

Paper author Paper submitter

Hi @nielsr , of course, we are improving the code and demo and will release them on HuggingFace. Thank you very much!

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