Introduction

This repository implements Lee et al., Hierarchical Novelty Detection for Visual Object Recognition, CVPR 2018 in PyTorch.

@inproceedings{lee2018hierarchical,
  title={Hierarchical Novelty Detection for Visual Object Recognition},
  author={Lee, Kibok and Lee, Kimin and Min, Kyle and Zhang, Yuting and Shin, Jinwoo and Lee, Honglak},
  booktitle={CVPR},
  year={2018}
}

Dependencies

Data

You may download either raw images or ResNet-101 features. If you download ResNet-101 features, place them in datasets/{dataset}/. ({dataset} = ImageNet, AWA2, CUB)

ImageNet

AWA, CUB

WordNet

You do not have to download the files, but we provide the source of them for your reference.

Preparation

Taxonomy

Run sh scripts/preparation.sh {d}. ({d} = imagenet_full, imagenet, awa2, cub)

Output files are in taxonomy/{dataset}/.

You can download pre-built taxonomies [here].

Feature extraction (ImageNet) or conversion (AWA, CUB)

Run sh scripts/feature.sh {d}. ({d} = imagenet, awa2, cub)

Output files are in datasets/{dataset}/.

If you have ResNet-101 features for ImageNet, skip this.

Train, test

Run sh scripts/train.sh {d} {m}. ({d} = imagenet, awa2, cub, {m} = relabel, td, loo, td+loo)

Output files are in train/.

You can download models reported in the paper [here].

Note