Part Segmentation on ShapeNet In-Context
87.9mIoUPIC-Cat + MICAS
Evaluation Results
| Method | Links | |
|---|---|---|
| PIC-Cat + MICASModel training/learning paradigm=In-context learning models, Venues=Ours2024.11 | 87.9 | |
| PIC-Sep + MICASModel training/learning paradigm=In-context learning models, Venues=Ours2024.11 | 86.8 | |
| PIC++Training Strategy=In-context Learning, Aggregation=-Sep2024.04 | 85.53 | |
| PIC++Training Strategy=In-context Learning, Aggregation=-Cat2024.04 | 85.27 | |
| PIC-S-CatModel training/learning paradigm=In-context learning models, Venues=Arxiv'242024.11 | 83.8 | |
| PIC-S-SepModel training/learning paradigm=In-context learning models, Venues=Arxiv'242024.11 | 83.7 | |
| ACTTraining Strategy=Task-Specific2024.04 | 81.24 | |
| ACTModel training/learning paradigm=Task-specific models (trained separately), Venues=ICLR'232024.11 | 81.2 | |
| PCTModel training/learning paradigm=Task-specific models (trained separately), Venues=CVM'212024.11 | 79.5 | |
| PCTTraining Strategy=Task-Specific2024.04 | 79.46 | |
| PIC-CatModel training/learning paradigm=In-context learning models, Venues=NeurIPS'232024.11 | 79 | |
| PICTraining Strategy=In-context Learning, Aggregation=-Cat2024.04 | 78.95 | |
| PointNetModel training/learning paradigm=Task-specific models (trained separately), Venues=CVPR'172024.11 | 77.5 | |
| PointNetTraining Strategy=Task-Specific2024.04 | 77.45 | |
| DGCNNTraining Strategy=Task-Specific2024.04 | 76.12 | |
| DGCNNModel training/learning paradigm=Task-specific models (trained separately), Venues=TOG' 192024.11 | 76.1 | |
| PIC-SepModel training/learning paradigm=In-context learning models, Venues=NeurIPS'232024.11 | 75 | |
| PICTraining Strategy=In-context Learning, Aggregation=-Sep2024.04 | 74.95 | |
| CopyModel training/learning paradigm=In-context learning models2024.11 | 24.2 | |
| CopyTraining Strategy=In-context Learning2024.04 | 24.18 | |
| I2P-MAEModel training/learning paradigm=Multi-task models: share backbone + multi-task heads, Venues=CVPR'232024.11 | 22.6 | |
| PCMamba‡Training Strategy=Multitask‡2024.04 | 19.53 | |
| DGCNNModel training/learning paradigm=Multi-task models: share backbone + multi-task heads, Venues=TOG' 192024.11 | 17 | |
| DGCNN†Training Strategy=Multitask†2024.04 | 16.95 | |
| PCT†Training Strategy=Multitask†2024.04 | 16.71 | |
| PCTModel training/learning paradigm=Multi-task models: share backbone + multi-task heads, Venues=CVM'212024.11 | 16.7 | |
| PointNet†Training Strategy=Multitask†2024.04 | 15.33 | |
| PointNetModel training/learning paradigm=Multi-task models: share backbone + multi-task heads, Venues=CVPR'172024.11 | 15.3 | |
| ACTModel training/learning paradigm=Multi-task models: share backbone + multi-task heads, Venues=ICLR'232024.11 | 12.1 | |
| ReConModel training/learning paradigm=Multi-task models: share backbone + multi-task heads, Venues=ICML 232024.11 | 7.7 | |
| PointMAE‡Training Strategy=Multitask‡2024.04 | 5.42 | |
| Point-MAEModel training/learning paradigm=Multi-task models: share backbone + multi-task heads, Venues=ECCV'222024.11 | 5.4 | |
| Point-BERTModel training/learning paradigm=In-context learning models, Venues=CVPR 222024.11 | 0.7 | |
| Point-BERTTraining Strategy=In-context Learning2024.04 | 0.65 |