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Weakly Supervised Open-Vocabulary Object Detection

This is an official implementation for AAAI2024 paper "Weakly Supervised Open-Vocabulary Object Detection". (Code is coming soon!)

📋 Table of content

1. 📎 Paper Link
2. 💡 Abstract
3. 📖 Method
4. 🛠️ Install
5. ✏️ Usage
   1. Start
   2. Prepare Datasets
   3. Training
   4. Inference
6. 🔍 Citation
7. ❤️ Acknowledgement

📎 Paper Link <a name="1"></a>

Read our arXiv Paper

💡 Abstract <a name="2"></a>

Despite weakly supervised object detection (WSOD) being a promising step toward evading strong instance-level annotations, its capability is confined to closed-set categories within a single training dataset. In this paper, we propose a novel weakly supervised open-vocabulary object detection framework, namely WSOVOD, to extend traditional WSOD to detect novel concepts and utilize diverse datasets with only image-level annotations. To achieve this, we explore three vital strategies, including dataset-level feature adaptation, image-level salient object localization, and region-level vision-language alignment. First, we perform data-aware feature extraction to produce an input-conditional coefficient, which is leveraged into dataset attribute prototypes to identify dataset bias and help achieve cross-dataset generalization. Second, a customized location-oriented weakly supervised region proposal network is proposed to utilize high-level semantic layouts from the category-agnostic segment anything model to distinguish object boundaries. Lastly, we introduce a proposal-concept synchronized multiple-instance network, i.e., object mining and refinement with visual-semantic alignment, to discover objects matched to the text embeddings of concepts. Extensive experiments on Pascal VOC and MS COCO demonstrate that the proposed WSOVOD achieves new state-of-the-art compared with previous WSOD methods in both close-set object localization and detection tasks. Meanwhile, WSOVOD enables cross-dataset and open-vocabulary learning to achieve on-par or even better performance than well-established fully-supervised open-vocabulary object detection (FSOVOD).

📖 Method <a name="3"></a>

The overall of our WSOVOD.

🛠️ Install <a name="4"></a>

conda create --name wsovod python=3.9
conda activate wsovod
pip install torch==1.12.1+cu113 torchvision==0.13.1+cu113 torchaudio==0.12.1 --extra-index-url https://download.pytorch.org/whl/cu113
pip install -r requirements.txt
pip install -e .

✏️ Usage <a name="5"></a>

1、Please follow this to prepare datasets for training.

2、Download SAM checkpoints.

mkdir tools/sam_checkpoints & cd tools/sam_checkpoints
wget https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth
wget https://dl.fbaipublicfiles.com/segment_anything/sam_vit_l_0b3195.pth
wget https://dl.fbaipublicfiles.com/segment_anything/sam_vit_b_01ec64.pth

3、Prepare SAM proposals for WSOVOD, take voc_2007_train for example.

bash scripts/generate_sam_proposals_cuda.sh 4 --checkpoint tools/sam_checkpoints/sam_vit_h_4b8939.pth --model-type vit_h --points-per-side 32 --pred-iou-thresh 0.86 --stability-score-thresh 0.92 --crop-n-layers 1 --crop-n-points-downscale-factor 2 --min-mask-region-area 20.0 --dataset-name voc_2007_train --output datasets/proposals/sam_voc_2007_train_d2.pkl

4、Prepare class text embeddings for WSOVOD, take COCO for example.

python tools/generate_class_text_embedding_cuda.py --dataset-name coco_2017_val --mode-type ViT-L/14/32 --prompt-type single --output models/coco_text_embedding_single_prompt.pkl

5、Download backbone pretrained from here.

6、Train a single dataset and test on another dataset, take COCO and VOC for example.

bash scripts/train_script.sh tools/train_net.py configs/COCO-Detection/WSOVOD_WSR_18_DC5_1x.yaml 4 20240301

python tools/train_net.py --config-file configs/PascalVOC-Detection/WSOVOD_WSR_18_DC5_1x.yaml --num-gpus 4 --eval-only MODEL.WEIGHTS output/configs/COCO-Detection/WSOVOD_WSR_50_DC5_1x_20240301/model_final.pth

7、Train mix datasets, take COCO and VOC for example.

🔍 Citation <a name="6"></a>

If you find WSOVOD useful in your research, please consider citing:

@InProceedings{WSOVOD_2024_AAAI,
	author = {Lin, Jianghang and Shen, Yunhang and Wang, Bingquan and Lin, Shaohui and Li, Ke and Cao, Liujuan},
	title = {Weakly Supervised Open-Vocabulary Object Detection},
	booktitle = {Proceedings of the AAAI Conference on Artificial Intelligence},
	year = {2024},
}   

License

WSOVOD is released under the Apache 2.0 license.

❤️ Acknowledgement <a name="7"></a>

• UWSOD
• detectron2