This repository contains the official PyTorch implementation of the paper "Label-Free Event-based Object Recognition via Joint Learning with Image Reconstruction from Events" paper (ICCV 2023, Oral). [Paper]

• CLIP (https://github.com/openai/CLIP)

Download N-Caltech101 datasets. Download N-ImageNet datasets.

For convenience, you can also use data split that we have used: Download N-Caltech101 & Caltech101 datasets. Download N-ImageNet100 & ImageNet100 datasets.

Our folder structure is as follows:

caltech-101 (For Image)
└── caltech-101
   └── 101_ObjectCategories
      ├── accordion
      │   ├── image_0001.jpg
      │   └── ...
      ├── airplanes
      │   ├── image_0001.jpg
      │   └── ...
      │ 
      └── ...

Caltech101 (For Event)
├── accordion
│   ├── image_0001.bin
│   └── ...
├── airplanes
│   ├── image_0001.bin
│   └── ...
└── ...


ImageNet (For Image)
├── extracted_100_train
│      ├── n01443537
│      │   ├── n01443537_2.JPEG
│      │   └── ...
│      └── ...
└── extracted_100_val
       ├── ILSVRC2012_val_00000007.JPEG
       ├── ILSVRC2012_val_00000017.JPEG
       └── ...

N_ImageNet (For Event)
├── extracted_100_train
│      ├── n01443537
│      │   ├── n01443537_2.npz
│      │   └── ...
│      └── ...
└── extracted_100_val
       ├── n01443537
       │   ├── ILSVRC2012_val_00000236.npz
       │   └── ...
       ├── n01616318
       │   ├── ILSVRC2012_val_00000018.npz
       │   └── ...
       │ 
       └── ...

datasets/caltech_event_ours_unpair_noise.py -L136: data_dir = "your caltech-101 path", event_dir = "your N-Caltech 101 path"

datasets/N_imagenet100_noise.py -L115: data_dir = "your ImageNet path", event_dir = "your N-ImageNet path"

Train & Test on N-Caltech 101 Dataset

    $ python pretraining_event_with_prototype_caltech.py -en $experiment_name$ -d caltech_ours --ssl_spatial --inverse --n_mask 6

Train & Test on N-ImageNet 100 Dataset

    $ python pretraining_event_with_prototype_imagenet.py -en $experiment_name$ -d imagenet100 --ssl_spatial --inverse --n_mask 6

You can also use the multi prototype by adding the --multi_proto

Hoonhee Cho*, Hyeonseong Kim*, Yujeong Chae, and Kuk-Jin Yoon "Label-Free Event-based Object Recognition via Joint Learning with Image Reconstruction from Events", In ICCV, 2023.

@inproceedings{cho2023label,
  title={Label-Free Event-based Object Recognition via Joint Learning with Image Reconstruction from Events},
  author={Cho, Hoonhee and Kim, Hyeonseong and Chae, Yujeong and Yoon, Kuk-Jin},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={19866--19877},
  year={2023}
}

If you have any question, please send an email to hoonhee cho ([email protected])

The project codes and datasets can be used for research and education only.