Code for the Grounded Visual Question Answering (GVQA) model from the below paper:
This is the code that we wrote to train the state-of-the-art VQA models described in our paper. Our ensemble of 7 models obtained 66.67% on real open-ended test-dev and 70.24% on real multiple-choice test-dev.
The MuRel network is a Machine Learning model learned end-to-end to answer questions about images. It relies on the object bounding boxes extracted from the image to build a complitely connected graph where each node corresponds to an object or region. The MuRel network contains a MuRel cell…
This is the code for the NeurIPS 2019 article available here: https://arxiv.org/abs/1906.10169.
The main features of cdancette/rubi.bootstrap.pytorch are: Language and Visual QA.
Open-source alternatives to cdancette/rubi.bootstrap.pytorch include: aishwaryaagrawal/gvqa — Code for the Grounded Visual Question Answering (GVQA) model from the below paper:. akirafukui/vqa-mcb — This is the code that we wrote to train the state-of-the-art VQA models described in our paper. Our ensemble of 7… cadene/murel.bootstrap.pytorch — The MuRel network is a Machine Learning model learned end-to-end to answer questions about images. It relies on the… cyanogenoid/vqa-counting — This is the official implementation of our ICLR 2018 paper [Learning to Count Objects in Natural Images for Visual… facebookresearch/clevr-iep — This is the code for the paper. a2zadeh/social-iq.