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

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

28 Jun 2024arXiv:2406.20076archive 2025-07-28

Yuxuan Zhang, Tianheng Cheng, Rui Hu, Lei Liu, Heng Liu, Longjin Ran, Xiaoxin Chen, Wenyu Liu, Xinggang Wang

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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Tasks

Interactive SegmentationLanguage ModelingLanguage ModellingReferring ExpressionReferring Expression SegmentationSegmentation

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Referring Expression Segmentation RefCOCO testA EVF-SAM Overall IoU 84.2 #4 of 13 Archive leaderboard report
Referring Expression Segmentation RefCOCO testB EVF-SAM Overall IoU 80.2 #4 of 13 Archive leaderboard report
Referring Expression Segmentation RefCOCO+ test B EVF-SAM Overall IoU 71.9 #3 of 30 Archive leaderboard report
Referring Expression Segmentation RefCOCO+ testA EVF-SAM Overall IoU 80 #4 of 30 Archive leaderboard report
Referring Expression Segmentation RefCOCO+ val EVF-SAM Overall IoU 76.5 #4 of 33 Archive leaderboard report
Referring Expression Segmentation RefCOCOg-test EVF-SAM Overall IoU 78.3 #5 of 18 Archive leaderboard report
Referring Expression Segmentation RefCOCOg-val EVF-SAM Overall IoU 78.2 #5 of 23 Archive leaderboard report
Referring Expression Segmentation RefCoCo val EVF-SAM Overall IoU 82.4 #6 of 37 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

AttentionCLIPSAMSoftmax

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