Few Shot Semantic Segmentation On Pascal 5I 5
المقاييس
Mean IoU
النتائج
نتائج أداء النماذج المختلفة على هذا المعيار القياسي
جدول المقارنة
| اسم النموذج | Mean IoU |
|---|---|
| hierarchical-dense-correlation-distillation | 71.8 |
| apanet-adaptive-prototypes-alignment-network | 62.6 |
| singular-value-fine-tuning-few-shot-1 | 69.8 |
| multi-similarity-based-hyperrelation-network | 72.3 |
| fecanet-boosting-few-shot-semantic | 66.7 |
| adaptive-prototype-learning-and-allocation | 64.36 |
| prior-guided-feature-enrichment-network-for | 61.4 |
| msdnet-multi-scale-decoder-for-few-shot | 68.7 |
| panet-few-shot-image-semantic-segmentation | 55.7 |
| dense-cross-query-and-support-attention | 68.5 |
| doubly-deformable-aggregation-of-covariance | 71.7 |
| self-support-few-shot-semantic-segmentation | 73.1 |
| mianet-aggregating-unbiased-instance-and-1 | 71.59 |
| self-calibrated-cross-attention-network-for | 71.5 |
| few-shot-segmentation-without-meta-learning-a | 66.6 |
| msanet-multi-similarity-and-attention-1 | 73.99 |
| mining-latent-classes-for-few-shot | 66.8 |
| self-guided-and-cross-guided-learning-for-few | 62.9 |
| pyramid-graph-networks-with-connection | 58.5 |
| masked-cross-image-encoding-for-few-shot | 70.03 |
| masked-cross-image-encoding-for-few-shot | 68.21 |
| self-support-few-shot-semantic-segmentation | 69.3 |
| intermediate-prototype-mining-transformer-for | 68.2 |
| dense-cross-query-and-support-attention | 74.9 |
| quaternion-valued-correlation-learning-for | 71.2 |
| singular-value-fine-tuning-few-shot-1 | 72.28 |
| hybrid-mamba-for-few-shot-segmentation | 74.1 |
| bridge-the-points-graph-based-few-shot | 82.6 |
| cost-aggregation-with-4d-convolutional-swin | 72 |
| anti-aliasing-semantic-reconstruction-for-few | 57.99 |
| clustered-patch-element-connection-for-few | 66.2 |
| prototype-as-query-for-few-shot-semantic | 67.4 |
| feature-proxy-transformer-for-few-shot | 78 |
| few-shot-segmentation-via-cycle-consistent | 66.6 |
| hmfs-hybrid-masking-for-few-shot-segmentation | 69.3 |
| intermediate-prototype-mining-transformer-for | 69.2 |
| mianet-aggregating-unbiased-instance-and-1 | 71.99 |
| simpler-is-better-few-shot-semantic | 63.7 |
| visual-and-textual-prior-guided-mask-assemble | 78.6 |
| self-calibrated-cross-attention-network-for | 70.3 |
| hypercorrelation-squeeze-for-few-shot | 70.4 |
| doubly-deformable-aggregation-of-covariance | 70.9 |
| hmfs-hybrid-masking-for-few-shot-segmentation | 67.1 |
| sg-one-similarity-guidance-network-for-one | 47.1 |
| eliminating-feature-ambiguity-for-few-shot | 74.2 |
| canet-class-agnostic-segmentation-networks | 57.1 |
| integrative-few-shot-learning-for | 71.1 |
| visual-and-textual-prior-guided-mask-assemble | 74.6 |
| self-guided-and-cross-guided-learning-for-few | 59.2 |
| hybrid-mamba-for-few-shot-segmentation | 71.1 |
| prototype-as-query-for-few-shot-semantic | 67 |
| msanet-multi-similarity-and-attention-1 | 70.4 |
| prior-guided-feature-enrichment-network-for | 61.9 |
| prototype-mixture-models-for-few-shot | 57.3 |
| doubly-deformable-aggregation-of-covariance | 65.7 |
| cost-aggregation-is-all-you-need-for-few-shot | 71.6 |
| clustered-patch-element-connection-for-few | 66.5 |
| msanet-multi-similarity-and-attention-1 | 72.6 |
| anti-aliasing-semantic-reconstruction-for-few | 60.96 |
| a-new-local-transformation-module-for-few | 60.6 |
| quaternion-valued-correlation-learning-for | 69.5 |
| quaternion-valued-correlation-learning-for | 64.2 |
| hierarchical-dense-correlation-distillation | 69.3 |
| simpler-is-better-few-shot-semantic | 64.7 |
| interclass-prototype-relation-for-few-shot | 70.2 |
| feature-proxy-transformer-for-few-shot | 73.7 |
| msdnet-multi-scale-decoder-for-few-shot | 70.8 |
| apanet-adaptive-prototypes-alignment-network | 68 |
| part-aware-prototype-network-for-few-shot | 62.0 |
| interclass-prototype-relation-for-few-shot | 70.9 |
| few-shot-segmentation-without-meta-learning-a | 65.6 |
| dense-gaussian-processes-for-few-shot | 75.4 |
| feature-weighting-and-boosting-for-few-shot | 55.1 |
| hypercorrelation-squeeze-for-few-shot | 64.1 |
| cost-aggregation-with-4d-convolutional-swin | 70.1 |
| seggpt-segmenting-everything-in-context | 89.8 |
| few-shot-semantic-segmentation-with-support | 68.5 |
| doubly-deformable-aggregation-of-covariance | 71.4 |
| hmfs-hybrid-masking-for-few-shot-segmentation | 68.2 |
| visual-and-textual-prior-guided-mask-assemble | 75.2 |
| fecanet-boosting-few-shot-semantic | 70 |
| hypercorrelation-squeeze-for-few-shot | 69.5 |
| doubly-deformable-aggregation-of-covariance | 73.3 |
| prior-guided-feature-enrichment-network-for | 59 |
| hmfs-hybrid-masking-for-few-shot-segmentation | 70.9 |
| eliminating-feature-ambiguity-for-few-shot | 70.6 |
| dense-gaussian-processes-for-few-shot | 73.5 |
| mining-latent-classes-for-few-shot | 69.3 |
| apanet-adaptive-prototypes-alignment-network | 66 |
| singular-value-fine-tuning-few-shot-1 | 69.11 |
| singular-value-fine-tuning-few-shot-1 | 71.82 |
| adaptive-prototype-learning-and-allocation | 63.94 |
| feature-weighting-and-boosting-for-few-shot | 59.9 |
| learning-what-not-to-segment-a-new | 70.91 |
| beyond-the-prototype-divide-and-conquer | 67.80 |
| dense-cross-query-and-support-attention | 68.3 |