By Dongze Hao, Qunbo Wang and Jing Liu

This is the official implementation of the paper. In this paper, we propose a Semantic-Visual Graph Reasoning framework (SVG) for VisDial. Specifically, we first construct a semantic graph to capture the semantic rela- tionships between different entities in the current question and the dialog history. Secondly, we construct a semantics-aware visual graph to capture high-level visual semantics including key objects of the image and their visual relationships. Exten- sive experimental results on the VisDial v0.9 and v1.0 show that our method has shown superior performance compared to the state-of-the-art models across most evaluation metrics.

conda create -n svg python=3.8
conda activate svg
conda conda install pytorch==1.7.0 torchvision==0.8.0 torchaudio==0.7.0 cudatoolkit=10.2 -c pytorch
pip install tqdm pyyaml nltk setproctitle

1. Download the data

• Download the VisDial v0.9 and v1.0 dialog json files from here and keep it under $PROJECT_ROOT/data/v0.9 and $PROJECT_ROOT/data/v1.0 directory, respectively.
• batra-mlp-lab provides the word counts for VisDial v1.0 train split visdial_1.0_word_counts_train.json. They are used to build the vocabulary. Keep it under $PROJECT_ROOT/data/v1.0 directory.
• batra-mlp-lab provides Faster-RCNN image features pre-trained on Visual Genome. Keep it under $PROJECT_ROOT/data/visdial_1.0_img and set argument img_feature_type to faster_rcnn_x101 in config/hparams.py file.
  • features_faster_rcnn_x101_train.h5: Bottom-up features of 36 proposals from images of train split.
  • features_faster_rcnn_x101_val.h5: Bottom-up features of 36 proposals from images of val split.
  • features_faster_rcnn_x101_test.h5: Bottom-up features of 36 proposals from images of test split.
• gicheonkang provides pre-extracted Faster-RCNN image features, which contain bounding boxes information. Set argument img_feature_type to dan_faster_rcnn_x101 in config/hparams.py file.
  • train_btmup_f.hdf5: Bottom-up features of 10 to 100 proposals from images of train split (32GB).
  • train_imgid2idx.pkl: image_id to bbox index file for train split
  • val_btmup_f.hdf5: Bottom-up features of 10 to 100 proposals from images of validation split (0.5GB).
  • val_imgid2idx.pkl: image_id to bbox index file for val split
  • test_btmup_f.hdf5: Bottom-up features of 10 to 100 proposals from images of test split (2GB).
  • test_imgid2idx.pkl: image_id to bbox index file for test split

2. Preprocess the data

• Download the GloVe pretrained word vectors from here, and keep glove.6B.300d.txt under $PROJECT_ROOT/data/word_embeddings/glove directory. Run

  python data/preprocess/init_glove.py

• Preprocesse textual inputs

  python data/data_utils.py

3. Train the model

   python main.py --model svg --version 1.0

4. Evaluate the model

    python main.py --model svg --evaluate /path/to/checkpoint.pth --eval_split val --version 1.0

This code is reimplemented as a fork of batra-mlp-lab/visdial-challenge-starter-pytorch and yuleiniu/rva.