Few-Shot Pattern Detection via Template Matching and Regression (ICCV25 Highlight)

Eunchan Jo, Dahyun Kang, Sanghyun Kim, Yunseon Choi, Minsu Cho

Official implementation of "Few-Shot Pattern Detection via Template Matching and Regression",
✨ ICCV 2025 (Highlight) ✨

[Project Page] [Paper] [Dataset] [Model weights]

Overview

Abstract:
We address the problem of few-shot pattern detection, which aims to detect all instances of a given pattern, typically represented by a few exemplars, from an input image. Although similar problems have been studied in few-shot object counting and detection (FSCD), previous methods and their benchmarks have narrowed patterns of interest to object categories and often fail to localize non-object patterns. In this work, we propose a simple yet effective detector based on template matching and regression, dubbed TMR. While previous FSCD methods typically represent target exemplars as spatially collapsed prototypes and lose structural information, we revisit classic template matching and regression. It effectively preserves and leverages the spatial layout of exemplars through a minimalistic structure with a small number of learnable convolutional or projection layers on top of a frozen backbone. We also introduce a new dataset, dubbed RPINE, which covers a wider range of patterns than existing object-centric datasets. Our method outperforms the state-of-the-art methods on the three benchmarks, RPINE, FSCD-147, and FSCD-LVIS, and demonstrates strong generalization in cross-dataset evaluation.

Installation and Data & Backbone weights Preparation

Note: this released version was tested on Python == 3.11.9, Pytorch == 2.4.0 and cuda == 12.1.

Install python dependencies:

pip install -r requirements.txt

* Data preparation

You can download FSCD-147 and FSCD-LVIS datasets from the Counting-DETR repository.

You can download RPINE dataset form this link.

* Backbone weights preparation

You can download SAM backbone weights from the SAM-HQ and SAM repositories.

Place them in the checkpoints folder as shown in the 'Main Repository Structure' section below.

Main Repository Structure

Template-Matching-And-Regression/
├── checkpoints/                      # place backbone checkpoints here
│   ├── sam_hq_vit_b.pth
│   ├── sam_hq_vit_h.pth
│   └── sam_vit_h_4b8939.pth
│   
├── weights\                          # place TMR weights here
│   ├── TMR_RPINE/best_model.ckpt
│   ├── TMR_FSCD147/best_model.ckpt
│   ├── TMR_FSCD_LVIS_seen/best_model.ckpt
│   └── TMR_FSCD_LVIS_unseen/best_model.ckpt
│   
├── scripts\                          
│   ├── train
│   └── eval
│   
└── others...

Model weights

Download the pre-trained model weights from this link and place them in the weights folder.

Demo

python demo.py --ckpt ./weights/TMR_FSCD147/best_model.ckpt

Training and Testing

Training

Please change the --datapath argument to your own path where you have stored the dataset.

sh scripts/train/TMR_RPINE.sh
sh scripts/train/TMR_FSCD147.sh
sh scripts/train/TMR_FSCD_LVIS_Seen.sh
sh scripts/train/TMR_FSCD_LVIS_Unseen.sh

For detailed argument descriptions, please refer to main.py.

Testing

To evaluate the pre-trained weights only, create a folder and copy the weight checkpoint into it, then change the --logpath argument to that folder's directory.

You can add the --refine_box argument to evaluate with the SAM decoder box refinement setting.

sh scripts/eval/TMR_RPINE.sh
sh scripts/eval/TMR_FSCD147.sh
sh scripts/eval/TMR_FSCD_LVIS_Seen.sh
sh scripts/eval/TMR_FSCD_LVIS_Unseen.sh

You can check the descriptions of all arguments used in the scripts by referring to main.py.

Citation

If you find this work useful for your research, please cite our paper:

@inproceedings{jo2025tmr,
  title     = {Few-Shot Pattern Detection via Template Matching and Regression},
  author    = {Eunchan Jo, Dahyun Kang, Sanghyun Kim, Yunseon Choi, and Minsu Cho},
  booktitle = {International Conference on Computer Vision (ICCV)},
  year      = {2025},
}