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[NeurIPS 2024] EZ-HOI: VLM Adaptation via Guided Prompt Learning for Zero-Shot HOI Detection

Paper Links

Dataset

Follow the process of UPT.

The downloaded files should be placed as follows. Otherwise, please replace the default path to your custom locations.

|- EZ-HOI
|   |- hicodet
|   |   |- hico_20160224_det
|   |       |- annotations
|   |       |- images
|   |- vcoco
|   |   |- mscoco2014
|   |       |- train2014
|   |       |-val2014
:   :

Dependencies

Follow the environment setup in UPT.
Follow the environment setup in ADA-CM.
run the python file to obtain the pre-extracted CLIP image features

python CLIP_hicodet_extract.py

Remember to make sure the correct path for annotation files and datasets.

|- EZ-HOI
|   |- hicodet_pkl_files
|   |   |- clip336_img_hicodet_test
|   |   |- clip336_img_hicodet_train
|   |   |- clipbase_img_hicodet_test
|   |   |- clipbase_img_hicodet_train
|   |- vcoco_pkl_files
|   |   |- clip336_img_vcoco_train
|   |   |- clip336_img_vcoco_val
:   :

HICO-DET

Train on HICO-DET:

bash scripts/hico_train_vitB_zs.sh

Test on HICO-DET:

bash scripts/hico_test_vitB_zs.sh

Model Zoo

Dataset	Setting	Backbone	mAP	Unseen	Seen
HICO-DET	UV	ResNet-50+ViT-B	32.32	25.10	33.49
HICO-DET	UV	ResNet-50+ViT-L	36.84	28.82	38.15
HICO-DET	RF	ResNet-50+ViT-B	33.13	29.02	34.15
HICO-DET	RF	ResNet-50+ViT-L	36.73	34.24	37.35
HICO-DET	NF	ResNet-50+ViT-B	31.17	33.66	30.55
HICO-DET	NF	ResNet-50+ViT-L	34.84	36.33	34.47
HICO-DET	UO	ResNet-50+ViT-B	32.27	33.28	32.06
HICO-DET	UO	ResNet-50+ViT-L	36.38	38.17	36.02

Dataset	Setting	Backbone	mAP	Rare	Non-rare
HICO-DET	default	ResNet-50+ViT-L	38.61	37.70	38.89

Citation

If you find our paper and/or code helpful, please consider citing :

@inproceedings{
lei2024efficient,
title={EZ-HOI: VLM Adaptation via Guided Prompt Learning for Zero-Shot HOI Detection},
author={Lei, Qinqian and Wang, Bo and Robby T., Tan},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024}
}

Acknowledgement

We gratefully thank the authors from UPT and ADA-CM for open-sourcing their code.