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[ICCV 2023] Efficient Adaptive Human-Object Interaction Detection with Concept-guided Memory

Dataset

Follow the process of UPT.

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

|- ADA-CM
|   |- hicodet
|   |   |- hico_20160224_det
|   |       |- annotations
|   |       |- images
|   |- vcoco
|   |   |- mscoco2014
|   |       |- train2014
|   |       |-val2014
:   :

Dependencies

Follow the environment setup in UPT.
Our code is built upon CLIP. Install the local package of CLIP:

cd CLIP && python setup.py develop && cd ..

Download the CLIP weights to checkpoints/pretrained_clip.

|- ADA-CM
|   |- checkpoints
|   |   |- pretrained_clip
|   |       |- ViT-B-16.pt
|   |       |- ViT-L-14-336px.pt
:   :

Download the weights of DETR and put them in checkpoints/.

Dataset	DETR weights
HICO-DET	weights
V-COCO	weights

|- ADA-CM
|   |- checkpoints
|   |   |- detr-r50-hicodet.pth
|   |   |- detr-r50-vcoco.pth
:   :   :

Pre-extracted Features

Download the pre-extracted features from HERE and the pre-extracted bboxes from HERE. The downloaded files have to be placed as follows.

|- ADA-CM
|   |- hicodet_pkl_files
|   |   |- union_embeddings_cachemodel_crop_padding_zeros_vitb16.p
|   |   |- hicodet_union_embeddings_cachemodel_crop_padding_zeros_vit336.p
|   |   |- hicodet_train_bbox_R50.p
|   |   |- hicodet_test_bbox_R50.p
|   |- vcoco_pkl_files
|   |   |- vcoco_union_embeddings_cachemodel_crop_padding_zeros_vit16.p
|   |   |- vcoco_union_embeddings_cachemodel_crop_padding_zeros_vit336.p
|   |   |- vcoco_train_bbox_R50.p
|   |   |- vcoco_test_bbox_R50.p
:   :

TrainingFree Mode

HICO-DET

python main_tip_ye.py --world-size 1 --pretrained checkpoints/detr-r50-hicodet.pth --output-dir checkpoints/test --eval --post_process --use_multi_hot --logits_type HO+U+T --num_shot 8 --file1 hicodet_pkl_files/union_embeddings_cachemodel_crop_padding_zeros_vitb16.p --clip_dir_vit checkpoints/pretrained_clip/ViT-B-16.pt

V-COCO

Cache detection results for evaluation on V-COCO:

python main_tip_ye.py --world-size 1 --dataset vcoco --data-root vcoco/ --partitions trainval test --pretrained checkpoints/detr-r50-vcoco.pth --output-dir matlab/TF_vcoco/ --num-workers 4 --cache --post_process --dic_key verb --use_multi_hot --num_shot 8 --logits_type HO+U+T --file1 vcoco_pkl_files/vcoco_union_embeddings_cachemodel_crop_padding_zeros_vit16.p

For V-COCO, we did not implement evaluation utilities, and instead use the utilities provided by Gupta et al.. Refer to these instructions for more details.

FineTuning Mode

HICO-DET

Train on HICO-DET:

python main_tip_finetune.py --world-size 1 --pretrained checkpoints/detr-r50-hicodet.pth --output-dir checkpoints/hico --use_insadapter --num_classes 117 --use_multi_hot --file1 hicodet_pkl_files/union_embeddings_cachemodel_crop_padding_zeros_vitb16.p --clip_dir_vit checkpoints/pretrained_clip/ViT-B-16.pt

Test on HICO-DET:

python main_tip_finetune.py --world-size 1 --pretrained checkpoints/detr-r50-hicodet.pth --output-dir checkpoints/hico --use_insadapter --num_classes 117 --use_multi_hot --file1 hicodet_pkl_files/union_embeddings_cachemodel_crop_padding_zeros_vitb16.p --clip_dir_vit checkpoints/pretrained_clip/ViT-B-16.pt --eval --resume CKPT_PATH

V-COCO

Training on V-COCO

python main_tip_finetune.py --world-size 1 --dataset vcoco --data-root vcoco/ --partitions trainval test --pretrained checkpoints/detr-r50-vcoco.pth --output-dir checkpoints/vcoco-injector-r50 --use_insadapter --num_classes 24 --use_multi_hot --file1 vcoco_pkl_files/vcoco_union_embeddings_cachemodel_crop_padding_zeros_vit16.p  --clip_dir_vit checkpoints/pretrained_clip/ViT-B-16.pt

Cache detection results for evaluation on V-COCO

python main_tip_finetune.py --world-size 1 --dataset vcoco --data-root vcoco/ --partitions trainval test --pretrained checkpoints/detr-r50-vcoco.pth --output-dir checkpoints/vcoco-injector-r50 --use_insadapter --num_classes 24 --use_multi_hot --file1 vcoco_pkl_files/vcoco_union_embeddings_cachemodel_crop_padding_zeros_vit16.p  --clip_dir_vit checkpoints/pretrained_clip/ViT-B-16.pt --cache --resume CKPT_PATH

Model Zoo

Dataset	Backbone	mAP	Rare	Non-rare	Weights
HICO-DET	ResNet-50+ViT-B	33.80	31.72	34.42	weights
HICO-DET	ResNet-50+ViT-L	38.40	37.52	38.66	weights

Dataset	Backbone	Scenario 1	Scenario 2	Weights
V-COCO	ResNet-50+ViT-B	56.12	61.45	weights
V-COCO	ResNet-50+ViT-L	58.57	63.97	weights

Citation

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

@article{ting2023hoi,
  title={Efficient Adaptive Human-Object Interaction Detection with Concept-guided Memory},
  author={Ting Lei and Fabian Caba and Qingchao Chen and Hailin Ji and Yuxin Peng and Yang Liu},
  year={2023},
  booktitle={ICCV},
  organization={IEEE},
}

Acknowledgement

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