@inproceedings{choi2019sdn, title = {Why Can't I Dance in the Mall? Learning to Mitigate Scene Bias in Action Recognition}, author = {Choi, Jinwoo and Gao, Chen and Messou, C. E. Joseph and Huang, Jia-Bin}, booktitle={NeurIPS}, year={2019} } 

pip install -r sdn_packages.txt 

 python train.py --video_path <your dataset dir path> \ --annotation_path <your dataset dir path>/kinetics.json \ --result_path <path to save your model> \ --root_path <your dataset dir path> \ --dataset kinetics \ --n_classes 200 \ --n_finetune_classes 200 \ --model resnet \ --model_depth 18 \ --resnet_shortcut A \ --batch_size 32 \ --val_batch_size 16 \ --n_threads 16 \ --checkpoint 1 \ --ft_begin_index 0 \ --is_mask_adv \ --learning_rate 0.0001 \ --weight_decay 1e-5 \ --n_epochs 100 \ --pretrain_path <pre-trained model file path> 

python train.py \ --video_path <your dataset dir path> \ --annotation_path <your dataset dir path>/kinetics.json \ --result_path <path to save your model> \ --root_path <your dataset dir path> \ --dataset kinetics_adv \ --n_classes 200 \ --n_finetune_classes 200 \ --model resnet \ --model_depth 18 \ --resnet_shortcut A \ --batch_size 32 \ --val_batch_size 16 \ --n_threads 16 \ --checkpoint 1 \ --ft_begin_index 0 \ --num_place_hidden_layers 3 \ --new_layer_lr 1e-2 \ --learning_rate 1e-4 \ --warm_up_epochs 5 \ --weight_decay 1e-5 \ --n_epochs 100 \ --place_pred_path <full path of your kinetics pseudo scene labels> \ --is_place_adv \ --is_place_soft \ --alpha 1.0 \ --is_mask_adv \ --num_places_classes 365 \ --pretrain_path <pre-trained model file path> 

python train.py \ --video_path <your dataset dir path> \ --annotation_path <your dataset dir path>/kinetics.json \ --result_path <path to save your model> \ --root_path <your dataset dir path> \ --dataset kinetics_adv_msk \ --n_classes 200 \ --n_finetune_classes 200 \ --model resnet \ --model_depth 18 \ --resnet_shortcut A \ --batch_size 32 \ --val_batch_size 16 \ --n_threads 16 \ --checkpoint 1 \ --ft_begin_index 0 \ --num_place_hidden_layers 3 \ --num_human_mask_adv_hidden_layers 1 \ --new_layer_lr 1e-4 \ --learning_rate 1e-4 \ --warm_up_epochs 0 \ --weight_decay 1e-5 \ --n_epochs 100 \ --place_pred_path <full path of your kinetics pseudo scene labels> \ --is_place_adv \ --is_place_soft \ --is_mask_entropy \ --alpha 0.5 \ --mask_ratio 1.0 \ --slower_place_mlp \ --not_replace_last_fc \ --num_places_classes 365 \ --human_dets_path <full path of your kinetics human detections> \ --pretrain_path <pre-trained model file path: e.g., your SDN model with scene adversarial loss only> 

python train.py \ --dataset diving48 \ --root_path <your dataset path> \ --video_path <your dataset path> \ --n_classes 200 \ --n_finetune_classes 48 \ --model resnet \ --model_depth 18 \ --resnet_shortcut A \ --ft_begin_index 0 \ --batch_size 32 \ --val_batch_size 16 \ --n_threads 4 \ --checkpoint 1 \ --learning_rate 0.005 \ --weight_decay 1e-5 \ --n_epochs $epoch_ft \ --is_mask_adv \ --annotation_path $anno_path \ --result_path <path to save your fine-tuned model> \ --pretrain_path <pre-trained model file path: e.g., your full SDN model path> 

python train.py \ --dataset diving48 \ --root_path <your dataset path> \ --video_path <your dataset path> \ --n_finetune_classes 48 \ --n_classes 48 \ --model resnet \ --model_depth 18 \ --resnet_shortcut A \ --batch_size 32 \ --val_batch_size 16 \ --n_threads 4 \ --test \ --test_subset val \ --no_train \ --no_val \ --is_mask_adv \ --annotation_path $anno_path \ --result_path <path (dir) to save your fine-tuned model> \ --resume_path <path (the model checkpoint file) to save your fine-tuned model> 

python utils/eval_diving48.py \ --annotation_path $anno_path \ --prediction_path <path to your test result file (val.json) generated from the test step>