Semantic Correspondence On Spair 71K
평가 지표
PCK
평가 결과
이 벤치마크에서 각 모델의 성능 결과
비교 표
| 모델 이름 | PCK |
|---|---|
| hyperpixel-flow-semantic-correspondence-with | 28.2 |
| telling-left-from-right-identifying-geometry | 68.5 |
| telling-left-from-right-identifying-geometry | 85.6 |
| cats-boosting-cost-aggregation-with | 59.8 |
| telling-left-from-right-identifying-geometry | 82.9 |
| semantic-correspondence-as-an-optimal | 35.6 |
| cleandift-diffusion-features-without-noise | 70.0 |
| cost-aggregation-is-all-you-need-for-few-shot | 54.2 |
| a-tale-of-two-features-stable-diffusion | 74.6 |
| cost-aggregation-with-4d-convolutional-swin | 55.5 |
| a-tale-of-two-features-stable-diffusion | 64.0 |
| multi-scale-matching-networks-for-semantic | 50.4 |
| integrative-feature-and-cost-aggregation-with | 64.4 |
| learning-to-compose-hypercolumns-for-visual | 37.3 |
| correspondence-networks-with-adaptive | 30.1 |
| unsupervised-semantic-correspondence-using | 45.4 |
| cleandift-diffusion-features-without-noise | 64.8 |
| semantic-correspondence-with-transformers | 49.9 |
| cleandift-diffusion-features-without-noise | 61.4 |
| convolutional-hough-matching-networks | 46.3 |
| transformatcher-match-to-match-attention-for | 53.7 |