The git contains the source code associated with our BMVC 2018 paper: "Attention is All We Need: Nailing Down Object-centric Attention for Egocentric Activity Recognition" The paper is available in here.
python main-run-rgb.py --dataset gtea_61
--stage 1
--trainDatasetDir ./dataset/gtea_61/split2/train
--outDir experiments
--seqLen 25
--trainBatchSize 32
--numEpochs 300
--lr 1e-3
--stepSize 25 75 150
--decayRate 0.1
--memSize 512
python main-run-rgb.py --dataset gtea61
--stage 2
--trainDatasetDir ./dataset/gtea_61/split2/train
--outDir experiments
--stage1Dict best_model_state_dict.pth
--seqLen 25
--trainBatchSize 32
--numEpochs 150
--lr 1e-4
--stepSize 25 75
--decayRate 0.1
--memSize 512
python main-run-flow.py --dataset gtea61
--trainDatasetDir ./dataset/gtea_61/split2/train
--outDir experiments
--stackSize 5
--trainBatchSize 32
--numEpochs 750
--lr 1e-2
--stepSize 150 300 500
--decayRate 0.5
python main-run-twoStream.py --dataset gtea61
--flowModel ./models/best_model_state_dict_flow_split2.pth
--rgbModel ./models/best_model_state_dict_rgb_split2.pth
--trainDatasetDir ./dataset/gtea_61/split2/train
--outDir experiments
--seqLen 25
--stackSize 5
--trainBatchSize 32
--numEpochs 250
--lr 1e-2
--stepSize 1
--decayRate 0.99
--memSize 512
python eval-run-rgb.py --dataset gtea61
--datasetDir ./dataset/gtea_61/split2/test
--modelStateDict best_model_state_rgb.pth
--seqLen 25
--memSize 512
python eval-run-rgb.py --dataset gtea61
--datasetDir ./dataset/gtea_61/split2/test
--modelStateDict best_model_state_flow.pth
--stackSize 5
--numSegs 5
python eval-run-twoStream-joint.py --dataset gtea61
--datasetDir ./dataset/gtea_61/split2/test
--modelStateDict best_model_state_twoStream.pth
--seqLen 25
--stackSize 5
--memSize 512
The models trained on the fixed split (S2) of GTEA 61 can be downloaded from the following links
The dataset can be downloaded from the following link:
http://www.cbi.gatech.edu/fpv/
Once the videos are downloaded, extract the frames and optical flow using the following implementation:
https://github.com/yjxiong/dense_flow
Run 'prepareGTEA61Dataset.py' script to make the dataset.
Alternatively, the frames and the corresponding warp optical flow of the GTEA 61 dataset can be downloaded from the following link