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Official Code for: "DC-SAM: In-Context Segment Anything in Images and Videos via Dual Consistency"

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<div align="center">
<h1>DC-SAM: In-Context Segment Anything in Images and Videos via Dual Consistency</h1>

[![arXiv](https://img.shields.io/badge/arXiv-2504.12080-b31b1b.svg)](https://arxiv.org/abs/2504.12080) 
[![Hugging Face Paper](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Paper-yellow)](https://huggingface.co/papers/2504.12080)
[![TPAMI](https://img.shields.io/badge/Journal-IEEE_TPAMI-blue.svg)](https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=34)

<div>
    <a href="https://jueduilingdu.github.io/" target="_blank">Mengshi Qi</a><sup>1</sup>, 
    <a href="https://scholar.google.com/citations?user=WANrgGwAAAAJ" target="_blank">Pengfei Zhu</a><sup>1</sup>, 
    <a href="https://lxtgh.github.io/" target="_blank">Xiangtai Li</a><sup>2</sup>, 
    Xiaoyang Bi<sup>1</sup>, 
    <a href="http://luqi.info/" target="_blank">Lu Qi</a><sup>3</sup>, 
    <a href="https://scholar.google.com/citations?user=A-vcjvUAAAAJ" target="_blank">Huadong Ma</a><sup>1</sup>, 
    <a href="https://faculty.ucmerced.edu/mhyang/" target="_blank">Ming-Hsuan Yang</a><sup>3</sup>
</div>

<div>
    <sup>1</sup>State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications<br />
    <sup>2</sup>Nanyang Technological University, Singapore&emsp;
    <sup>3</sup>University of California, Merced
</div>

<p align="justify">
    <i>This repository provides the official IEEE TPAMI implementation of <b>DC-SAM</b>. We propose a novel dual-consistency framework to enable Segment Anything Models (SAM) to perform in-context segmentation across both images and videos. By enforcing spatial and temporal consistency, DC-SAM achieves superior generalization for zero-shot and interactive segmentation tasks without requiring extensive per-scene fine-tuning.</i>
</p>

</div>

## Table of Contents

* [News](#news)
* [Highlights](#highlights)
* [Benchmark](#benchmark)
* [Results](#results)
* [Getting Started](#getting-started)
* [Training & Evaluation](#training--evaluation)
* [Citation](#citation)
* [License](#license)

## News
**[2025/12/17]** Our Paper is accepted by IEEE TPAMI!

**[2025/03/17]** The validation set for IC-VOS is now released. Enjoy exploring and working with it!

**[2025/02/08]** The code and dataset for Image-to-image / Image-to-video In-Context Learning is released. Enjoy it:)

## Highlights

* **Dual Consistency SAM (DC-SAM) for one-shot segmentation**: Fully explores positive and negative features of visual prompts, generating high-quality prompts tailored for one-shot segmentation tasks.
* **Query cyclic-consistent cross-attention mechanism**: Ensures precise focus on key areas, effectively filtering out confusing components and improving accuracy and specificity in one-shot segmentation.
* **New video in-context segmentation benchmark (IC-VOS)**: Introduces a manually collected benchmark from existing video datasets, providing a robust platform for evaluating state-of-the-art methods.
* **Extension to SAM2 with mask tube design**: Enhances prompt generation for video object segmentation, achieving strong performance on the proposed IC-VOS benchmark.

## Benchmark

We establish a rigorous benchmark for **In-Context Video Object Segmentation (IC-VOS)** by adapting several classic datasets into the in-context paradigm. In this setting, the model must segment the target object in a query video based on a provided *reference frame + mask* pair.

---

## Results

**DC-SAM** significantly outperforms existing in-context learners and SAM-based variants by maintaining superior consistency.

## Getting Started

**Step 1**: clone this repository:

```
git clone https://github.com/zaplm/DC-SAM.git && cd DC-SAM
```

**Step 2**: create a conda environment and install the dependencies:
```
conda create -n DCSAM python=3.10 -y
conda activate DCSAM
conda install pytorch==2.4.0 torchvision==0.19.0 torchaudio==2.4.0 pytorch-cuda=12.1 -c pytorch -c nvidia -y
cd segment-anything-2 && pip install -e .
cd ../segment-anything && pip install -e .
cd .. && pip install -r requirements.txt
```

## Preparing Datasets

Download following datasets:

> #### 1. PASCAL-5<sup>i</sup>
> Download PASCAL VOC2012 devkit (train/val data):
> ```bash
> wget http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar
> ```
> Download PASCAL VOC2012 SDS extended mask annotations from [[Google Drive](https://drive.google.com/file/d/10zxG2VExoEZUeyQl_uXga2OWHjGeZaf2/view?usp=sharing)].

> #### 2. COCO-20<sup>i</sup>
> Download COCO2014 train/val images and annotations: 
> ```bash
> wget http://images.cocodataset.org/zips/train2014.zip
> wget http://images.cocodataset.org/zips/val2014.zip
> wget http://images.cocodataset.org/annotations/annotations_trainval2014.zip
> ```
> Download COCO2014 train/val annotations. train2014.zip [[Google Drive](https://drive.google.com/file/d/1cwup51kcr4m7v9jO14ArpxKMA4O3-Uge/view?usp=sharing)], [[val2014.zip](https://drive.google.com/file/d/1PNw4U3T2MhzAEBWGGgceXvYU3cZ7mJL1/view?usp=sharing)].
> (and locate both train2014/ and val2014/ under annotations/ directory).

#### 3. IC-VOS
Download our proposed IC-VOS validation set from [[Baidu Netdisk](https://pan.baidu.com/s/1Rjtqc1LLozLPar0t8m9qHQ?pwd=i8af)]/[[HuggingFace dataset](https://huggingface.co/datasets/zaplm/IC-VOS)]. (COCO2014 train/val annotations and PASCAL VOC2012 SDS extended mask annotations can also be accessed via our [[HuggingFace dataset](https://huggingface.co/datasets/zaplm/IC-VOS)])

Create a directory '../dataset' for the above few-shot segmentation datasets and appropriately place each dataset to have following directory structure:

Datasets/
      ├── VOC2012/            # PASCAL VOC2012 devkit
      │   ├── Annotations/
      │   ├── ImageSets/
      │   ├── ...
      │   └── SegmentationClassAug/
      ├── COCO2014/           
      │   ├── annotations/
      │   │   ├── train2014/  # (dir.) training masks (from Google Drive) 
      │   │   ├── val2014/    # (dir.) validation masks (from Google Drive)
      │   │   └── ..some json files..
      │   ├── train2014/
      │   └── val2014/
      └── IC-VOS/
          ├── 0a43a414/
          │   ├── Annotations/
          │   └── JPEGImages/
          └── ...

## Preparing Pretrained Weights

ResNet-50/101_v2 can be download from [Google Drive](https://drive.google.com/drive/folders/1Hrz1wOxOZm4nIIS7UMJeL79AQrdvpj6v)

VGG-16_bn can be download from [here](https://download.pytorch.org/models/vgg16_bn-6c64b313.pth)

SAM checkpoint can be download from this [repository](https://github.com/facebookresearch/segment-anything?tab=readme-ov-file#model-checkpoints:~:text=First%20download%20a-,model%20checkpoint,-.%20Then%20the%20model)

SAM2 checkpoint can be download from this [repository](https://github.com/facebookresearch/sam2)

## Training & Evaluation

### Training

It is recommend to train DC-SAM using 4 GPUs:

Train on image:
```
python3 -m torch.distributed.launch --nproc_per_node=4 --master_port=6224 train_image.py --epochs 50/100 --benchmark coco/pascal --lr 1e-4/2e-4 --bsz 2 --nshot 1 \
   --num_query 25 --sam_version 1/2 --nworker 8 --fold 0/1/2/3 --backbone resnet50/vgg16 --logpath log_name
```

Train on video (pretrain or finetune of image-to-video):
```
python3 -m torch.distributed.launch --nproc_per_node=4 --master_port=6224 train_video.py --epochs 40/10 --benchmark coco_all/coco_mask_tube --lr 1e-4/1e-5 --bsz 8/1 --nshot 1 \
   --num_query 25 --data_type image/video --sam_version 2 --nworker 8 --backbone resnet50 --logpath log_name
```

### Validation

To evaluate on the IC-VOS:
```
python eval_video.py --coco_path /path/to/coco --icvos_path /path/to/icvos --ckpt /path/to/ckpt
```

To evaluate on few-shot segmentation benchmarks:
```
python eval_iamge.py --datapath /path/to/benchmark --benchmark pascal/coco --fold 0/1/2/3 --ckpt /path/to/ckpt
```

## Citation

```
@article{qi2025dc,
  title={DC-SAM: In-Context Segment Anything in Images and Videos via Dual Consistency},
  author={Qi, Mengshi and Zhu, Pengfei and Ma, Huadong and Qi, Lu and Li, Xiangtai and Yang, Ming-Hsuan},
  journal={arXiv preprint arXiv:2504.12080},
  year={2025}
}
```

## License

This repository is licensed under [Apache 2.0](LICENSE).

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