In this guide, we will walk through the implementation of a visual question answering (VQA) model based on attention mechanisms, inspired by “Stacked Attention Networks for Image Question Answering” by Yang et al. (CVPR16). The model processes an image, reads a question, and provides an answer while also highlighting areas in the image relevant to the answer through a heatmap.

1. Train Your Own Network

To build your own model, follow these steps:

1.1 Extract Image Features

The model utilizes VGG-19 for extracting image features, which speeds up the training process significantly since we don’t fine-tune the CNN. You can download the model and extract features using the provided script.

sh scripts/download_vgg19.sh
th prepro_img.lua -image_root path_to_coco_images -gpuid 0

1.2 Preprocess VQA Dataset

Preprocessing is essential to prepare your dataset for training. You can pass a split of 1 to train and evaluate on the validation set, or 2 to train on both training and validation datasets. Use the following commands:

cd data
python vqa_preprocessing.py --download True --split 1
cd ..
python prepro.py --input_train_json data/vqa_raw_train.json --input_test_json data/vqa_raw_test.json --num_ans 1000

1.3 Training

Now that the data is ready, it’s time to train the model using:

th train.lua

2. Use a Pretrained Model

If you prefer to use a pretrained model instead of training your own, here’s how to get started:

2.1 Pretrained Models and Data Files

All necessary files can be downloaded from the designated repository:

• san1_2.t7: Model with 1 attention layer (SAN-1)
• san2_2.t7: Model with 2 attention layers (SAN-2)
• params_1.json: Vocabulary for training on train, evaluating on val
• params_2.json: Vocabulary for train+val
• qa_1.h5, qa_2.h5: QA features
• img_train_1.h5, img_test_1.h5: Image features

2.2 Running Evaluation

To evaluate the model, execute the following command:

model_path=checkpoints/model.t7
qa_h5=data/qa.h5
params_json=data/params.json
img_test_h5=data/img_test.h5
th eval.lua

This will generate a JSON file containing question IDs and predicted answers. Use VQA Evaluation Tools for assessing accuracy on the validation set, or submit to the VQA evaluation server on EvalAI for test results.

3. Results

The output results will be displayed in a structured format, showing the original image, the attention heatmap, and the image overlaid with attention. The predicted question and answer will be visible below the examples.

3.1 Quantitative Results

The model’s performance can be summarized in a table format. Here’s an example of what the results might look like:

VQA v2.0 Method             val       test
SAN-1                     53.15    55.28
SAN-2                     52.82    -
[d-LSTM + n-I]           51.62    54.22
[HieCoAtt]               54.57    -
[MCB]                    59.14    -

Troubleshooting

While running the model, you might encounter issues. Here are some tips to troubleshoot:

• Ensure that all datasets are downloaded properly before preprocessing.
• Check that you’re using compatible versions of PyTorch and other dependencies.
• If the model fails to generate evaluations, verify the paths to data files are correctly set.
• Adjust the num_attention_layers parameter for better accuracy if required.

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At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.