SAM Struggles in Concealed Scenes -- Empirical Study on Segment Anything
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866916366736424960 |
|---|---|
| author | Ji, Ge-Peng Fan, Deng-Ping Xu, Peng Cheng, Ming-Ming Zhou, Bowen Van Gool, Luc |
| author_facet | Ji, Ge-Peng Fan, Deng-Ping Xu, Peng Cheng, Ming-Ming Zhou, Bowen Van Gool, Luc |
| contents | Segmenting anything is a ground-breaking step toward artificial general intelligence, and the Segment Anything Model (SAM) greatly fosters the foundation models for computer vision. We could not be more excited to probe the performance traits of SAM. In particular, exploring situations in which SAM does not perform well is interesting. In this report, we choose three concealed scenes, i.e., camouflaged animals, industrial defects, and medical lesions, to evaluate SAM under unprompted settings. Our main observation is that SAM looks unskilled in concealed scenes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2304_06022 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | SAM Struggles in Concealed Scenes -- Empirical Study on Segment Anything Ji, Ge-Peng Fan, Deng-Ping Xu, Peng Cheng, Ming-Ming Zhou, Bowen Van Gool, Luc Computer Vision and Pattern Recognition Segmenting anything is a ground-breaking step toward artificial general intelligence, and the Segment Anything Model (SAM) greatly fosters the foundation models for computer vision. We could not be more excited to probe the performance traits of SAM. In particular, exploring situations in which SAM does not perform well is interesting. In this report, we choose three concealed scenes, i.e., camouflaged animals, industrial defects, and medical lesions, to evaluate SAM under unprompted settings. Our main observation is that SAM looks unskilled in concealed scenes. |
| title | SAM Struggles in Concealed Scenes -- Empirical Study on Segment Anything |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2304.06022 |