We lifted 3 functions out of this paper's own repositories and ran 3 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| hustvl/evf-sam | canonical | 1 of 1 |
| copy not recorded | — | 2 of 2 |
| Function | Status | Where it lives |
|---|---|---|
| dice_loss | Ran | this paper's copy was not recorded; identical code first harvested from maverickren/pixellm pointer only · get_code("a1c2083ec2eaeb22") |
| parse_args | Ran | hustvl/evf-sam/inference.py code served (permissive licence) · get_code("736601618e338997") |
| sigmoid_ce_loss | Ran | this paper's copy was not recorded; identical code first harvested from maverickren/pixellm pointer only · get_code("a9292f5d89194794") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
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.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2406.20076")
get_code_for_paper("2406.20076")
have("2406.20076")
Connect an agent — have() is free.