爱丁堡大学人工智能专业硕士论文项目【国外】.zip

# Making Machines More Human: A Multitask Learning Approach to VQA and Human Attention Prediction My MSc Dissertation Project had the goal of developing a Deep Learning algorithm capable of improving VQA performance of a state-of-the-art architecture while mimicking human attention, using the [VQA-HAT dataset](https://computing.ece.vt.edu/~abhshkdz/vqa-hat/). The Project was successful and some results can be found below. **The Dissertation PDF can be found in this repository - [msc-dissertation.pdf](https://github.com/goncalomcorreia/vqa_human_attention/blob/master/msc-dissertation.pdf).** Code adapted from [Stacked attention networks for image question answering](http://arxiv.org/abs/1511.02274). ## Dependencies The code is in python and uses Theano package. - Python 2.7 - Theano - Numpy - h5py ## Usage To train a model, ``` cd src/scripts; python mtl_san_deepfix.py ``` There is another README.md inside src describing the files there. ## Results Some results can be found below. "Human Attention and Answer" is the ground-truth. "SAN" is our main baseline - the Stacked Attention Network. Our main algorithm is "MTL SAN+DeepFix", able to improve VQA accuracy of our baseline SAN, while mimicking human attention. Remaining models are different baselines. Thorough explanations can be found in msc-dissertation.pdf