Tasks SotA Datasets Papers Methods Submit About

Papers With Code 2

A community resource for machine learning research: papers, code, benchmarks, and state-of-the-art results.

Explore

Notable Benchmarks All SotA Datasets Papers Methods

Community

Submit Results About

Data sourced from the PWC Archive (CC-BY-SA 4.0). Built by the community, for the community.

Models/VAT (ResNet-101)

VAT (ResNet-101)

Reported on 36 benchmarks across 3 tasks · 1 paper · 12 SOTA

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Methodology24 results

Few-Shot LearningonFSS-1000 (5-shot)
FB-IoU· 2022-07-22
94.4
SOTA
Cost Aggregation with 4D Convolutional Swin Transformer for Few-Shot Segmentation arXiv:2207.10866
Few-Shot LearningonFSS-1000 (5-shot)
Mean IoU· 2022-07-22
90.8
best: 91.7 (DACM (ResNet-101))
SOTA
Cost Aggregation with 4D Convolutional Swin Transformer for Few-Shot Segmentation arXiv:2207.10866
Few-Shot LearningonFSS-1000 (1-shot)
FB-IoU· 2022-07-22
94
SOTA
Cost Aggregation with 4D Convolutional Swin Transformer for Few-Shot Segmentation arXiv:2207.10866
Few-Shot LearningonFSS-1000 (1-shot)
Mean IoU· 2022-07-22
90.3
best: 90.8 (DACM (ResNet-101))
SOTA
Cost Aggregation with 4D Convolutional Swin Transformer for Few-Shot Segmentation arXiv:2207.10866
Meta-LearningonFSS-1000 (5-shot)
FB-IoU· 2022-07-22
94.4
SOTA
Cost Aggregation with 4D Convolutional Swin Transformer for Few-Shot Segmentation arXiv:2207.10866
Meta-LearningonFSS-1000 (5-shot)
Mean IoU· 2022-07-22
90.8
best: 91.7 (DACM (ResNet-101))
SOTA
Cost Aggregation with 4D Convolutional Swin Transformer for Few-Shot Segmentation arXiv:2207.10866
Meta-LearningonFSS-1000 (1-shot)
FB-IoU· 2022-07-22
94
SOTA
Cost Aggregation with 4D Convolutional Swin Transformer for Few-Shot Segmentation arXiv:2207.10866
Meta-LearningonFSS-1000 (1-shot)
Mean IoU· 2022-07-22
90.3
best: 90.8 (DACM (ResNet-101))
SOTA
Cost Aggregation with 4D Convolutional Swin Transformer for Few-Shot Segmentation arXiv:2207.10866
Few-Shot LearningonCOCO-20i (5-shot)
FB-IoU· 2022-07-22
72.4
best: 79.4 (PGMA-Net (ResNet-101))
Cost Aggregation with 4D Convolutional Swin Transformer for Few-Shot Segmentation arXiv:2207.10866
Few-Shot LearningonCOCO-20i (5-shot)
Mean IoU· 2022-07-22
47.9
best: 67.9 (SegGPT (ViT))
Cost Aggregation with 4D Convolutional Swin Transformer for Few-Shot Segmentation arXiv:2207.10866
Few-Shot LearningonPASCAL-5i (1-Shot)
FB-IoU· 2022-07-22
79.6
best: 86.2 (PGMA-Net (ResNet-101))
Cost Aggregation with 4D Convolutional Swin Transformer for Few-Shot Segmentation arXiv:2207.10866
Few-Shot LearningonPASCAL-5i (1-Shot)
Mean IoU· 2022-07-22
67.9
best: 83.2 (SegGPT (ViT))
Cost Aggregation with 4D Convolutional Swin Transformer for Few-Shot Segmentation arXiv:2207.10866
Few-Shot LearningonCOCO-20i (1-shot)
FB-IoU· 2022-07-22
68.8
best: 78.5 (PGMA-Net (ResNet-101))
Cost Aggregation with 4D Convolutional Swin Transformer for Few-Shot Segmentation arXiv:2207.10866
Few-Shot LearningonCOCO-20i (1-shot)
Mean IoU· 2022-07-22
41.3
best: 59.4 (PGMA-Net (ResNet-101))
Cost Aggregation with 4D Convolutional Swin Transformer for Few-Shot Segmentation arXiv:2207.10866
Few-Shot LearningonPASCAL-5i (5-Shot)
FB-IoU· 2022-07-22
83.2
best: 86.9 (PGMA-Net (ResNet-101))
Cost Aggregation with 4D Convolutional Swin Transformer for Few-Shot Segmentation arXiv:2207.10866
Few-Shot LearningonPASCAL-5i (5-Shot)
Mean IoU· 2022-07-22
72
best: 89.8 (SegGPT (ViT))
Cost Aggregation with 4D Convolutional Swin Transformer for Few-Shot Segmentation arXiv:2207.10866
Meta-LearningonCOCO-20i (5-shot)
FB-IoU· 2022-07-22
72.4
best: 79.4 (PGMA-Net (ResNet-101))
Cost Aggregation with 4D Convolutional Swin Transformer for Few-Shot Segmentation arXiv:2207.10866
Meta-LearningonCOCO-20i (5-shot)
Mean IoU· 2022-07-22
47.9
best: 67.9 (SegGPT (ViT))
Cost Aggregation with 4D Convolutional Swin Transformer for Few-Shot Segmentation arXiv:2207.10866
Meta-LearningonPASCAL-5i (1-Shot)
FB-IoU· 2022-07-22
79.6
best: 86.2 (PGMA-Net (ResNet-101))
Cost Aggregation with 4D Convolutional Swin Transformer for Few-Shot Segmentation arXiv:2207.10866
Meta-LearningonPASCAL-5i (1-Shot)
Mean IoU· 2022-07-22
67.9
best: 83.2 (SegGPT (ViT))
Cost Aggregation with 4D Convolutional Swin Transformer for Few-Shot Segmentation arXiv:2207.10866
Meta-LearningonCOCO-20i (1-shot)
FB-IoU· 2022-07-22
68.8
best: 78.5 (PGMA-Net (ResNet-101))
Cost Aggregation with 4D Convolutional Swin Transformer for Few-Shot Segmentation arXiv:2207.10866
Meta-LearningonCOCO-20i (1-shot)
Mean IoU· 2022-07-22
41.3
best: 59.4 (PGMA-Net (ResNet-101))
Cost Aggregation with 4D Convolutional Swin Transformer for Few-Shot Segmentation arXiv:2207.10866
Meta-LearningonPASCAL-5i (5-Shot)
FB-IoU· 2022-07-22
83.2
best: 86.9 (PGMA-Net (ResNet-101))
Cost Aggregation with 4D Convolutional Swin Transformer for Few-Shot Segmentation arXiv:2207.10866
Meta-LearningonPASCAL-5i (5-Shot)
Mean IoU· 2022-07-22
72
best: 89.8 (SegGPT (ViT))
Cost Aggregation with 4D Convolutional Swin Transformer for Few-Shot Segmentation arXiv:2207.10866

Computer Vision12 results

Few-Shot Semantic SegmentationonFSS-1000 (5-shot)
FB-IoU· 2022-07-22
94.4
SOTA
Cost Aggregation with 4D Convolutional Swin Transformer for Few-Shot Segmentation arXiv:2207.10866
Few-Shot Semantic SegmentationonFSS-1000 (5-shot)
Mean IoU· 2022-07-22
90.8
best: 91.7 (DACM (ResNet-101))
SOTA
Cost Aggregation with 4D Convolutional Swin Transformer for Few-Shot Segmentation arXiv:2207.10866
Few-Shot Semantic SegmentationonFSS-1000 (1-shot)
FB-IoU· 2022-07-22
94
SOTA
Cost Aggregation with 4D Convolutional Swin Transformer for Few-Shot Segmentation arXiv:2207.10866
Few-Shot Semantic SegmentationonFSS-1000 (1-shot)
Mean IoU· 2022-07-22
90.3
best: 90.8 (DACM (ResNet-101))
SOTA
Cost Aggregation with 4D Convolutional Swin Transformer for Few-Shot Segmentation arXiv:2207.10866
Few-Shot Semantic SegmentationonCOCO-20i (5-shot)
FB-IoU· 2022-07-22
72.4
best: 79.4 (PGMA-Net (ResNet-101))
Cost Aggregation with 4D Convolutional Swin Transformer for Few-Shot Segmentation arXiv:2207.10866
Few-Shot Semantic SegmentationonCOCO-20i (5-shot)
Mean IoU· 2022-07-22
47.9
best: 67.9 (SegGPT (ViT))
Cost Aggregation with 4D Convolutional Swin Transformer for Few-Shot Segmentation arXiv:2207.10866
Few-Shot Semantic SegmentationonPASCAL-5i (1-Shot)
FB-IoU· 2022-07-22
79.6
best: 86.2 (PGMA-Net (ResNet-101))
Cost Aggregation with 4D Convolutional Swin Transformer for Few-Shot Segmentation arXiv:2207.10866
Few-Shot Semantic SegmentationonPASCAL-5i (1-Shot)
Mean IoU· 2022-07-22
67.9
best: 83.2 (SegGPT (ViT))
Cost Aggregation with 4D Convolutional Swin Transformer for Few-Shot Segmentation arXiv:2207.10866
Few-Shot Semantic SegmentationonCOCO-20i (1-shot)
FB-IoU· 2022-07-22
68.8
best: 78.5 (PGMA-Net (ResNet-101))
Cost Aggregation with 4D Convolutional Swin Transformer for Few-Shot Segmentation arXiv:2207.10866
Few-Shot Semantic SegmentationonCOCO-20i (1-shot)
Mean IoU· 2022-07-22
41.3
best: 59.4 (PGMA-Net (ResNet-101))
Cost Aggregation with 4D Convolutional Swin Transformer for Few-Shot Segmentation arXiv:2207.10866
Few-Shot Semantic SegmentationonPASCAL-5i (5-Shot)
FB-IoU· 2022-07-22
83.2
best: 86.9 (PGMA-Net (ResNet-101))
Cost Aggregation with 4D Convolutional Swin Transformer for Few-Shot Segmentation arXiv:2207.10866
Few-Shot Semantic SegmentationonPASCAL-5i (5-Shot)
Mean IoU· 2022-07-22
72
best: 89.8 (SegGPT (ViT))
Cost Aggregation with 4D Convolutional Swin Transformer for Few-Shot Segmentation arXiv:2207.10866