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FOCUS: Familiar Objects in Common and Uncommon Settings

Repository for the ICML 2022 paper of the same name [link].

Instructions

Dataset

  1. Download the dataset from here. Or alternately, use the following command:
curl -L -o focus.zip https://umd.box.com/s/w7tvxer0wur7vtsoqcemfopgshn6zklv
  1. Unzip the dataset:
unzip focus.zip

Setup

  1. Clone this repository:
git clone https://github.com/priyathamkat/focus.git
  1. Add src to your PYTHONPATH:
export PYTHONPATH="$PYTHONPATH:/path/to/focus/src"
  1. Use FOCUS as follows:
from experiments.dataset import Focus
focus = Focus(
    path_to_focus,
    categories=[
        "truck",
        "car",
        "plane",
        "ship",
        "cat",
        "horse",
        "horse",
        "deer",
        "frog",
        "bird",
    ],
    times=["day"],
    weathers=["sunny"],
    locations=["grass", "street"],
    transform=None
)

See src/experiments/dataset.py for the valid arguments that can be passed to times, weathers and locations. Also, checkout src/experiments/focus-dataset.ipynb for more details about how to use the dataset.

What else is in this repo?

  1. src/experiments/evaluate_model.py - For evaluating a pretrained model on FOCUS (Section 4.2 in the paper).

  2. src/experiments/finetune.py - For finetuning a model on FOCUS (Section 4.3 in the paper).

  3. src/experiments/grad_cam_visualizations.ipynb - GradCAM visualizations (Figure 4 in the paper).

ImageNet Attributes

Machine generated environmental attributes for images in ImageNet can be downloaded from here (See Section 4.4 in our paper for more details).

Citation

If you use FOCUS, please consider citing our work:


@InProceedings{pmlr-v162-kattakinda22a,
  title = 	 {{FOCUS}: Familiar Objects in Common and Uncommon Settings},
  author =       {Kattakinda, Priyatham and Feizi, Soheil},
  booktitle = 	 {Proceedings of the 39th International Conference on Machine Learning},
  pages = 	 {10825--10847},
  year = 	 {2022},
  editor = 	 {Chaudhuri, Kamalika and Jegelka, Stefanie and Song, Le and Szepesvari, Csaba and Niu, Gang and Sabato, Sivan},
  volume = 	 {162},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {17--23 Jul},
  publisher =    {PMLR},
  pdf = 	 {https://proceedings.mlr.press/v162/kattakinda22a/kattakinda22a.pdf},
  url = 	 {https://proceedings.mlr.press/v162/kattakinda22a.html},
}