Awesome

VL-commonsense

Datasets

Data sets and code to mine data from Visual Genome are in mine-data directory. Also contains eval_dataset.py for comparing the VG dataset with CoDa and Wikipedia.

Models

We train two models: Distilled and CaptionBERT. The model checkpoints can be accessed at this link

Probing

The probing directory contains code for zero shot evaluation ("best template" mode) -- eval_zero_shot.py -- and the logistic regression classification case -- eval_classification.py. emb_for_size.py contains code for the "adjective projection" method for size evaluation. plot_snippet.py contains the plotting scripts for the heatmap and the dot-to-dot linked plot for individual objects.

Run files in probing from the parent directory. E.g.

python probing/eval_zero_shot.py

Prompt Tuning

Code for soft prompt tuning are in the soft-prompts directory. (source reference: https://github.com/hiaoxui/soft-prompts). Custom evaluation ("average template" case in the paper) is in soft-prompts/soft_prompts/run/model_eval.py. Run eval with config-vl-eval.yaml and prompt training with config-vl.yaml. See the README there for more information.

Citation

If this code is useful in you research, please cite

@article{zhang2022visual,
  title={Visual Commonsense in Pretrained Unimodal and Multimodal Models},
  author={Zhang, Chenyu and Van Durme, Benjamin and Li, Zhuowan and Stengel-Eskin, Elias},
  journal={arXiv preprint arXiv:2205.01850},
  year={2022}
}