Awesome

Ask_Attend_and_Answer

Ask, Attend and Answer: Exploring Question-Guided Spatial Attention for Visual Question Answering

Code

Instructions for training and testing the "SMem-VQA Two-Hop" model:

1. Download the provided caffe folder and install caffe following the instructions in http://caffe.berkeleyvision.org/installation.html .

2. Download MSCOCO images, and VQA annotations and questions:

   cd example/data/

   ./get_image.sh

3. Generate the hdf5 data for training and testing:

   cd example/

   python ./data/generate_h5_data/generate_h5_data.py

4. Train the model:

   cd example/

   run ./train/train_mm.sh

5. Model trained on VQA dataset: SMem-VQA

6. Predict the answers for the images and questions in VQA test-dev dataset:

   cd example/

   python ./prediction/predict_json.py

Citation

@inproceedings{xu2016ask,
    title = {Ask, attend and answer: Exploring question-guided spatial attention for visual question answering},
    author = {Xu, Huijuan and Saenko, Kate},
    booktitle = {European Conference on Computer Vision},
    year = {2016}
}