We lifted 15 functions out of this paper's own repositories and ran 9 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 |
|---|---|---|
| xzz2/pa-sam | canonical | 9 of 15 |
| Function | Status | Where it lives |
|---|---|---|
| all_gather | Ran | xzz2/pa-sam/utils/misc.py pointer only (licence: NONE) · get_code("899f5dd03c569d81") |
| get_im_gt_name_dict | Ran | xzz2/pa-sam/utils/dataloader.py pointer only (licence: NONE) · get_code("6377d2b0f1cb0b28") |
| get_rel_pos | Ran | xzz2/pa-sam/segment_anything_training/modeling/image_encoder.py pointer only (licence: NONE) · get_code("733d7f0bedcb74c2") |
| get_uncertain_point_coords_with_randomness | Ran | xzz2/pa-sam/utils/losses.py pointer only (licence: NONE) · get_code("752d76eca7bb4a97") |
| masks_to_boxes | Ran | xzz2/pa-sam/utils/misc.py pointer only (licence: NONE) · get_code("cab0cc7a7011655b") |
| point_sample | Ran | xzz2/pa-sam/utils/losses.py pointer only (licence: NONE) · get_code("965de439489202e7") |
| reduce_dict | Ran | xzz2/pa-sam/utils/misc.py pointer only (licence: NONE) · get_code("4183d276c611a490") |
| window_partition | Ran | xzz2/pa-sam/segment_anything_training/modeling/image_encoder.py pointer only (licence: NONE) · get_code("105fa08885dc36cc") |
| window_unpartition | Ran | xzz2/pa-sam/segment_anything_training/modeling/image_encoder.py pointer only (licence: NONE) · get_code("50f37d517e2be27f") |
| build_sam_vit_b | Not yet run | xzz2/pa-sam/segment_anything_training/build_sam.py pointer only (licence: NONE) · get_code("f9bc5f31ce61cee8") |
| build_sam_vit_h | Not yet run | xzz2/pa-sam/segment_anything_training/build_sam.py pointer only (licence: NONE) · get_code("6b77c1f11fff3ed6") |
| build_sam_vit_l | Not yet run | xzz2/pa-sam/segment_anything_training/build_sam.py pointer only (licence: NONE) · get_code("3f8890e695469246") |
| cat | Not yet run | xzz2/pa-sam/utils/losses.py pointer only (licence: NONE) · get_code("3e4ba19cff1075ba") |
| create_dataloaders | Not yet run | xzz2/pa-sam/utils/dataloader.py pointer only (licence: NONE) · get_code("7b785ed7aad4a361") |
| point_sample | Not yet run | xzz2/pa-sam/utils/function.py pointer only (licence: NONE) · get_code("ab9b6bc249e53154") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
The Segment Anything Model (SAM) has exhibited outstanding performance in various image segmentation tasks. Despite being trained with over a billion masks, SAM faces challenges in mask prediction quality in numerous scenarios, especially in real-world contexts. In this paper, we introduce a novel prompt-driven adapter into SAM, namely Prompt Adapter Segment Anything Model (PA-SAM), aiming to enhance the segmentation mask quality of the original SAM. By exclusively training the prompt adapter, PA-SAM extracts detailed information from images and optimizes the mask decoder feature at both sparse and dense prompt levels, improving the segmentation performance of SAM to produce high-quality masks. Experimental results demonstrate that our PA-SAM outperforms other SAM-based methods in high-quality, zero-shot, and open-set segmentation. We're making the source code and models available at https://github.com/xzz2/pa-sam.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2401.13051")
get_code_for_paper("2401.13051")
have("2401.13051")
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