MATLAB 2015a or newer (for native python support) MatConvNet beta19 http://www.vlfeat.org/matconvnet/
- Create a data directory
- Setup directory paths as specified in globals.m. Relevant tarballs specified in comments
- Clone the VQA API into the data directory and setup annotations
- Install matconvnet beta19 (17 or higher should work) and specify path in startup.m (with CuDNN enabled)
- Download the vgg-s model from: http://www.vlfeat.org/matconvnet/models/imagenet-vgg-s.mat
- Create results directory to store model snapshots from training
- Download and extract text feature caches to top level directory (wget http://xor.cs.illinois.edu/~kevin/wtl_cache_feats/word2vec_cache_utils.tar.gz)
word_and_vision_regions_inner_network.m : running this should initialize training. Results stored in opts.train.expDir word_and_vision_regions_inner_network_init.m: constructs the network mcqMaxMarginLossLayer.m: Loss layer implementation regionsProjectInnerLayer2.m: region selection layer implementation determiner_list.m: list of removed stopwords removed from questions globals.m: contains global paths to where cached features are stored.
run visualize_on_held_out.m to visualize results on the held-out set. The held out set comprises 10% of the training data from the train set. Our test model can be downloaded from: http://xor.cs.illinois.edu/~kevin/wtl_cache_feats/wtl_trainval_model.mat
word2vec_cache_utils: directory that holds caches of pre-processed question and answers utils: misc utility functions
This code is provided for academic use only.
If you have any questions about the code, feel free to contact Kevin Shih at firstname.lastname@example.org.