Few Shot Semantic Segmentation On Coco 20I 1
평가 지표
Mean IoU
평가 결과
이 벤치마크에서 각 모델의 성능 결과
비교 표
| 모델 이름 | Mean IoU |
|---|---|
| quaternion-valued-correlation-learning-for | 42.3 |
| feature-proxy-transformer-for-few-shot | 47 |
| intermediate-prototype-mining-transformer-for | 42.6 |
| panet-few-shot-image-semantic-segmentation | 20.9 |
| feature-proxy-transformer-for-few-shot | 42 |
| msanet-multi-similarity-and-attention-1 | 46.44 |
| intermediate-prototype-mining-transformer-for | 43 |
| mining-latent-classes-for-few-shot | 37.5 |
| adaptive-prototype-learning-and-allocation | 34.56 |
| singular-value-fine-tuning-few-shot-1 | 43.76 |
| diffss-diffusion-model-for-few-shot-semantic | 43.6 |
| apanet-adaptive-prototypes-alignment-network | 41.9 |
| self-support-few-shot-semantic-segmentation | 42 |
| matcher-segment-anything-with-one-shot-using | 52.7 |
| quaternion-valued-correlation-learning-for | 43.6 |
| eliminating-feature-ambiguity-for-few-shot | 46.4 |
| hmfs-hybrid-masking-for-few-shot-segmentation | 44.7 |
| mining-latent-classes-for-few-shot | 35.1 |
| msdnet-multi-scale-decoder-for-few-shot | 48.5 |
| prototype-as-query-for-few-shot-semantic | 45.7 |
| hmfs-hybrid-masking-for-few-shot-segmentation | 44.3 |
| prior-guided-feature-enrichment-network-for | 32.4 |
| singular-value-fine-tuning-few-shot-1 | 48.47 |
| visual-and-textual-prior-guided-mask-assemble | 59.4 |
| cost-aggregation-is-all-you-need-for-few-shot | 41.3 |
| clustered-patch-element-connection-for-few | 38.1 |
| doubly-deformable-aggregation-of-covariance | 40.6 |
| hierarchical-dense-correlation-distillation | 50 |
| msanet-multi-similarity-and-attention-1 | 50.45 |
| hmfs-hybrid-masking-for-few-shot-segmentation | 43.2 |
| fecanet-boosting-few-shot-semantic | 35.4 |
| msi-maximize-support-set-information-for-few | 49.8 |
| self-calibrated-cross-attention-network-for | 48.2 |
| cost-aggregation-with-4d-convolutional-swin | 41.3 |
| self-guided-and-cross-guided-learning-for-few | 37 |
| dense-cross-query-and-support-attention | 50.9 |
| simpler-is-better-few-shot-semantic | 32.9 |
| beyond-the-prototype-divide-and-conquer | 41.39 |
| anti-aliasing-semantic-reconstruction-for-few | 33.85 |
| few-shot-segmentation-via-cycle-consistent | 40.3 |
| fecanet-boosting-few-shot-semantic | 41.6 |
| hmfs-hybrid-masking-for-few-shot-segmentation | 45.9 |
| self-support-few-shot-semantic-segmentation | 37.4 |
| dense-cross-query-and-support-attention | 43.5 |
| iterative-few-shot-semantic-segmentation-from | 37.7 |
| doubly-deformable-aggregation-of-covariance | 43 |
| dense-cross-query-and-support-attention | 43.3 |
| part-aware-prototype-network-for-few-shot | 29.0 |
| interclass-prototype-relation-for-few-shot | 46.9 |
| msdnet-multi-scale-decoder-for-few-shot | 46.5 |
| visual-and-textual-prior-guided-mask-assemble | 54.3 |
| hybrid-mamba-for-few-shot-segmentation | 49.1 |
| singular-value-fine-tuning-few-shot-1 | 42.24 |
| dense-gaussian-processes-for-few-shot | 45 |
| integrative-few-shot-learning-for | 43.1 |
| self-calibrated-cross-attention-network-for | 46.3 |
| apanet-adaptive-prototypes-alignment-network | 40.5 |
| hmfs-hybrid-masking-for-few-shot-segmentation | 46.5 |
| dense-gaussian-processes-for-few-shot | 46.7 |
| mianet-aggregating-unbiased-instance-and-1 | 47.66 |
| simpler-is-better-few-shot-semantic | 32.4 |
| hypercorrelation-squeeze-for-few-shot | 39.2 |
| unleashing-the-potential-of-the-diffusion | 52.2 |
| eliminating-feature-ambiguity-for-few-shot | 51.3 |
| few-shot-segmentation-without-meta-learning-a | 34.1 |
| masked-cross-image-encoding-for-few-shot | 44.22 |
| hybrid-mamba-for-few-shot-segmentation | 52.1 |
| few-shot-semantic-segmentation-with-support | 41.4 |
| bridge-the-points-graph-based-few-shot | 58.7 |
| interclass-prototype-relation-for-few-shot | 45.3 |
| diffss-diffusion-model-for-few-shot-semantic | 46.7 |
| prior-guided-feature-enrichment-network-for | 34.1 |
| prototype-mixture-models-for-few-shot | 30.6 |
| apanet-adaptive-prototypes-alignment-network | 37.2 |
| prototype-as-query-for-few-shot-semantic | 47 |
| hierarchical-dense-correlation-distillation | 45.9 |
| seggpt-segmenting-everything-in-context | 56.1 |
| clustered-patch-element-connection-for-few | 38.3 |
| hypercorrelation-squeeze-for-few-shot | 41.2 |
| feature-weighting-and-boosting-for-few-shot | 21.2 |
| learning-what-not-to-segment-a-new | 46.23 |
| iterative-few-shot-semantic-segmentation-from | 42.4 |
| mianet-aggregating-unbiased-instance-and-1 | 45.69 |
| feature-weighting-and-boosting-for-few-shot | 20.02 |
| singular-value-fine-tuning-few-shot-1 | 48.02 |