1. Introduction
2. Main Results
3. Usage
4. Acknowledgments
5. Contacts

Abstract: We tackle a new task of few-shot object counting and detection. Given a few exemplar bounding boxes of a target object class, we seek to count and detect all objects of the target class. This task shares the same supervision as the few-shot object counting but additionally outputs the object bounding boxes along with the total object count. To address this challenging problem, we introduce a novel two-stage training strategy and a novel uncertainty-aware few-shot object detector: \Approach. The former is aimed at generating pseudo ground-truth bounding boxes to train the latter. The latter leverages the pseudo ground-truth provided by the former but takes the necessary steps to account for the imperfection of pseudo ground-truth. To validate the performance of our method on the new task, we introduce two new datasets named FSCD-147 and FSCD-LVIS. Both datasets contain images with complex scenes, multiple object classes per image, and a huge variation in object shapes, sizes, and appearance. Our proposed approach outperforms very strong baselines adapted from few-shot object counting and few-shot object detection with a large margin in both counting and detection metrics.

Details of the Counting-DETR model architecture and experimental results can be found in our following paper: Counting-DETR

@inproceedings{countingdetr2022,
title     = {{Few-shot Object Counting and Detection}},
author    = {Thanh Nguyen, Chau Pham, Khoi Nguyen and Minh Hoai},
booktitle = {Proceedings of the European Conference on Computer Vision 2022},
year      = {2022}
}

Please CITE our paper when Counting-DETR is used to help produce published results or incorporated into other software.

For experiments on the FSCD-147 dataset

For experiments on the FSCD-LVIS dataset

First, pull the docker with the following command:

docker pull quaden/docker_images:pytorch_cuda102

Second, create a container

docker run -it  --name od_cnt --gpus=all  --shm-size=8G --volume="$PWD:/workspace/" --ipc=host -v /tmp/.X11-unix:/tmp/.X11-unix -e DISPLAY=$DISPLAY  pytorch_cuda102:latest  /bin/bash

First, clone this git repo inside the docker container

git clone [email protected]:VinAIResearch/Counting-DETR.git

Second, download our FSCD-147 and FSCD-LVIS datasets from the below link:

https://drive.google.com/drive/folders/14qzZaV4S8EBUj3yEkgrDQC7iErHxSPjl?usp=sharing

In case, above link doens't work, use following link:

https://drive.google.com/drive/folders/1tlHZIg6X3jp6qARTxKh0kMsNvuIQop9P?usp=sharing

Extract each dataset for each dataset to the corresponding folder. For example, to conduct experiment for 1st stage of FSCD-147 dataset, extract FSCD_147.zip to src/CountDETR_147_1st_stage. Folder structure should be like:

Counting-DETR
│   README.md # This is the Readme you're reading now
│   LICENSE    
└───src
│   └───CountDETR_147_1st_stage # all expriements for 1st-stage of FSCD-147 dataset is conducted here
│   |    │   FSCD_147 # extracted from FSCD_147.zip
│   |    │   main.py # source code for 1st stage
│   |    │   ...
│   |
│   └───CountDETR_147_2nd_stage # all expriements for 2nd-stage of FSCD-147 dataset is conducted here
│   |    │   FSCD_147 # extracted from FSCD_147.zip
│   |    │   main.py # source code for 2nd stage
│   |    │   ...
...

Then, change the directory to the corresponding experiments and run the corresponding scripts. Sample scripts would both train and evaluate experiments.

For the 1st stage in FSCD-147 experiments:

cd src/CountDETR_147_1st_stage && ./scripts/weakly_supervise_fscd_147.sh

For the 2st stage in FSCD-147 experiments:

cd src/CountDETR_147_2nd_stage && ./scripts/var_wh_laplace_600.sh

For the 1st stage in FSCD-LVIS experiments:

cd src/CountDETR_lvis_1st_stage && ./scripts/lvis_1_stage.sh

For the 2st stage in FSCD-LVIS experiments:

cd src/CountDETR_lvis_2nd_stage && ./scripts/var_wh_laplace_lvis_2nd.sh

Our code borrowed some parts of the official repositories of AnchorDETR. Thank you so much to the authors for their efforts to release source code and pre-trained weights.

If you have any questions, feel free to open an issue or contact us at [email protected].