This is an experimental Torch implementation of the VIS + LSTM visual question answering model from the paper Exploring Models and Data for Image Question Answering by Mengye Ren, Ryan Kiros & Richard Zemel.
Requirements:
Download the MSCOCO train+val images and VQA data using sh data/download_data.sh. Extract all the downloaded zip files inside the data folder.
unzip Annotations_Train_mscoco.zip
unzip Questions_Train_mscoco.zip
unzip train2014.zip
unzip Annotations_Val_mscoco.zip
unzip Questions_Val_mscoco.zip
unzip val2014.zip
If you had them downloaded already, copy over the train2014 and val2014 image folders
and VQA JSON files to the data folder.
Download the VGG-19 Caffe model and prototxt using sh models/download_models.sh.
- To avoid memory issues with LuaJIT, install Torch with Lua 5.1 (
TORCH_LUA_VERSION=LUA51 ./install.sh). More instructions here. - If working with plain Lua, luaffifb may be needed for loadcaffe, unless using pre-extracted fc7 features.
th extract_fc7.lua -split train
th extract_fc7.lua -split val
batch_size: Batch size. Default is 10.split: train/val. Default istrain.gpuid: 0-indexed id of GPU to use. Default is -1 = CPU.proto_file: Path to thedeploy.prototxtfile for the VGG Caffe model. Default ismodels/VGG_ILSVRC_19_layers_deploy.prototxt.model_file: Path to the.caffemodelfile for the VGG Caffe model. Default ismodels/VGG_ILSVRC_19_layers.caffemodel.data_dir: Data directory. Default isdata.feat_layer: Layer to extract features from. Default isfc7.input_image_dir: Image directory. Default isdata.
th train.lua
rnn_size: Size of LSTM internal state. Default is 512.num_layers: Number of layers in LSTMembedding_size: Size of word embeddings. Default is 512.learning_rate: Learning rate. Default is 4e-4.learning_rate_decay: Learning rate decay factor. Default is 0.95.learning_rate_decay_after: In number of epochs, when to start decaying the learning rate. Default is 15.alpha: Alpha for adam. Default is 0.8beta: Beta used for adam. Default is 0.999.epsilon: Denominator term for smoothing. Default is 1e-8.batch_size: Batch size. Default is 64.max_epochs: Number of full passes through the training data. Default is 15.dropout: Dropout for regularization. Probability of dropping input. Default is 0.5.init_from: Initialize network parameters from checkpoint at this path.save_every: No. of iterations after which to checkpoint. Default is 1000.train_fc7_file: Path to fc7 features of training set. Default isdata/train_fc7.t7.fc7_image_id_file: Path to fc7 image ids of training set. Default isdata/train_fc7_image_id.t7.val_fc7_file: Path to fc7 features of validation set. Default isdata/val_fc7.t7.val_fc7_image_id_file: Path to fc7 image ids of validation set. Default isdata/val_fc7_image_id.t7.data_dir: Data directory. Default isdata.checkpoint_dir: Checkpoint directory. Default ischeckpoints.savefile: Filename to save checkpoint to. Default isvqa.gpuid: 0-indexed id of GPU to use. Default is -1 = CPU.
th predict.lua -checkpoint_file checkpoints/vqa_epoch23.26_0.4610.t7 -input_image_path data/train2014/COCO_train2014_000000405541.jpg -question 'What is the cat on?'
checkpoint_file: Path to model checkpoint to initialize network parameters froinput_image_path: Path to input imagequestion: Question string
Randomly sampled image-question pairs from the VQA test set, and answers predicted by the VIS+LSTM model.
Q: What animals are those? A: Sheep
Q: What color is the frisbee that's upside down? A: Red
Q: What is flying in the sky? A: Kite
Q: What color is court? A: Blue
Q: What is in the standing person's hands? A: Bat
Q: Are they riding horses both the same color? A: No
Q: What shape is the plate? A: Round
Q: Is the man wearing socks? A: Yes
Q: What is over the woman's left shoulder? A: Fork
Q: Where are the pink flowers? A: On wall
- Last hidden layer image features from VGG-19
- Zero-padded question sequences for batched implementation
- Training questions are filtered for
top_nanswers,top_n = 1000by default (~87% coverage)
To reproduce results shown on this page or try your own
image-question pairs, download the following and run
predict.lua with the appropriate paths.
- vqa_epoch23.26_0.4610.t7 (Serialized using Lua51) [GPU] [CPU]
- answers_vocab.t7
- questions_vocab.t7
- data.t7
- Exploring Models and Data for Image Question Answering, Ren et al., NIPS15
- VQA: Visual Question Answering, Antol et al., ICCV15
MIT