This is the official PyTorch implementation.

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• Related research work published in 2024.01 ~ 2024.06 is summarized. Five papers are added to the list below.
• The usage of FG3D dataset (TIP 2021) is supported in this repository.
• VSFormer (TVCG 2024) is added to this benchmark.
• Related research work published in 2023 is summarized. One paper is added to the list below.
• Related multi-view-based feature aggregation methods for biomedical tasks will not be summarized here.
• New hypergraph-based methods and soft label methods published from 2023 will no longer be summarized here unless explicitly designed for multi-view object classification.

• Discussion on the usage of allow_tf32.

  torch.backends.cuda.matmul.allow_tf32 = False
  torch.backends.cudnn.allow_tf32 = False
  

  • Except for CVR (ICCV 2021), the performance of other methods is almost unaffected.
  • The performance change for CVR cannot be neglected and may affect its best configuration.
  • We recommend trying the following configurations to get the best one when doing custom training on the MVP-N dataset.

  -CVR_K=2 -CVR_LAMBDA=1
  -CVR_K=3 -CVR_LAMBDA=0.5
  -CVR_K=3 -CVR_LAMBDA=1
  

• Modifications in the summary file compared to the paper content.
  • The codes of MVT (BMVC 2021) and iMHL (TIP 2018) are released.
  • MVT (BMVC 2021) satisfies P1 by analyzing its open-source implementation.
• There is a typo in the caption of Table 4 (NeurIPS 2022), which should be corrected as 'Backbone (ResNet-18): 11.20 M, 10.91 G, and 6.19 ± 0.05 ms'.

Period: 2015.01 ~ 2024.06
Conferences: NeurIPS, ICLR, ICML, CVPR, ICCV, ECCV, AAAI, IJCAI, MM, WACV, BMVC, ACCV
Journals: TPAMI, IJCV, TIP, TNNLS, TMM, TCSVT, TVCG, PR
Workshops: NeurIPS, ICLR, ICML, CVPR, ICCV, ECCV

Ubuntu 20.04.3 LTS
Python 3.8.10
CUDA 11.1
cuDNN 8
NVIDIA GeForce RTX 3090 GPU
Intel(R) Core(TM) i9-10900X CPU @ 3.70GHz

Step 1: Get repository

git clone https://github.com/SMNUResearch/MVP-N.git
cd MVP-N

Step 2: Install dependencies

sh install.sh

Step 1: Get dataset documentation from [Google Drive]
Step 2: Download data.zip from [Google Drive]
Step 3: Place data.zip in this repository
Step 4: Unzip data.zip

unzip data.zip

Step 1: Download pretrained weights from [Google Drive]
Step 2: Place weights.zip in this repository
Step 3: Unzip weights.zip

unzip weights.zip -d weights

Step 4: Evaluation

# feature aggregation performance
python3 main_multi_view.py -MV_FLAG=TEST -MV_TYPE=DAN -MV_TEST_WEIGHT=./weights/DAN.pt

# confusion matrix
python3 main_multi_view.py -MV_FLAG=CM -MV_TYPE=DAN -MV_TEST_WEIGHT=./weights/DAN.pt

# computational efficiency
python3 main_multi_view.py -MV_FLAG=COMPUTATION -MV_TYPE=DAN

# soft label performance
python3 main_single_view.py -SV_FLAG=TEST -SV_TYPE=SAT -SV_TEST_WEIGHT=./weights/SAT.pt

Training with default configurations

# feature aggregation
python3 main_multi_view.py -MV_FLAG=TRAIN -MV_TYPE=DAN
python3 main_multi_view.py -MV_FLAG=TRAIN -MV_TYPE=CVR
python3 main_multi_view.py -MV_FLAG=TRAIN -MV_TYPE=SMVCNN -SMVCNN_USE_EMBED

# soft label
python3 main_single_view.py -SV_FLAG=TRAIN -SV_TYPE=KD
python3 main_single_view.py -SV_FLAG=TRAIN -SV_TYPE=HPIQ
python3 main_single_view.py -SV_FLAG=TRAIN -SV_TYPE=HS

Training with other configurations

# feature aggregation
python3 main_multi_view.py -MV_FLAG=TRAIN -MV_TYPE=CVR -CVR_LAMBDA=0.5 -CVR_K=3

# soft label
python3 main_single_view.py -SV_FLAG=TRAIN -SV_TYPE=KD -KD_T=3

Details of configurations are provided in config/base.yaml

Step 1: Download FG3D.zip from [Google Drive]
Step 2: Place FG3D.zip in this repository
Step 3: Unzip FG3D.zip

unzip FG3D.zip

Step 4: Training with default configurations. Details are provided in config/FG3D.yaml

python3 main_multi_view_FG3D.py -MV_FLAG=TRAIN -MV_TYPE=DAN -NUM_CLASSES=13 -CLASSES=Airplane
python3 main_multi_view_FG3D.py -MV_FLAG=TRAIN -MV_TYPE=SMVCNN -NUM_CLASSES=20 -CLASSES=Car
python3 main_multi_view_FG3D.py -MV_FLAG=TRAIN -MV_TYPE=VSF -NUM_CLASSES=33 -CLASSES=Chair