Segment Anything in High Quality

Segment Anything in High Quality

Segment Anything in High Quality
Lei Ke, Mingqiao Ye, Martin Danelljan, Yifan Liu, Yu-Wing Tai, Chi-Keung Tang, Fisher Yu
ETH Zurich & HKUST

We propose HQ-SAM to upgrade SAM for high-quality zero-shot segmentation. Refer to our paper for more details. Our code and models will be released in two weeks. Stay tuned!

Visual comparison between SAM and HQ-SAM

SAM vs. HQ-SAM

Introduction

The recent Segment Anything Model (SAM) represents a big leap in scaling up segmentation models, allowing for powerful zero-shot capabilities and flexible prompting. Despite being trained with 1.1 billion masks, SAM's mask prediction quality falls short in many cases, particularly when dealing with objects that have intricate structures. We propose HQ-SAM, equipping SAM with the ability to accurately segment any object, while maintaining SAM's original promptable design, efficiency, and zero-shot generalizability. Our careful design reuses and preserves the pre-trained model weights of SAM, while only introducing minimal additional parameters and computation. We design a learnable High-Quality Output Token, which is injected into SAM's mask decoder and is responsible for predicting the high-quality mask. Instead of only applying it on mask-decoder features, we first fuse them with early and final ViT features for improved mask details. To train our introduced learnable parameters, we compose a dataset of 44K fine-grained masks from several sources. HQ-SAM is only trained on the introduced detaset of 44k masks, which takes only 4 hours on 8 GPUs. We show the efficacy of HQ-SAM in a suite of 9 diverse segmentation datasets across different downstream tasks, where 7 out of them are evaluated in a zero-shot transfer protocol.

Quantitative comparison between SAM and HQ-SAM

Note: For box-prompting-based evaluation, we feed SAM and our HQ-SAM with the same image/video bounding boxes and adopt the single mask output mode of SAM.

Various ViT backbones on COCO:

Note: For the COCO dataset, we use a SOTA detector FocalNet-DINO trained on the COCO dataset as our box prompt generator.

YTVIS and HQ-YTVIS

Note:Using ViT-L backbone. We adopt the SOTA detector Mask2Former trained on the YouTubeVIS 2019 dataset as our video boxes prompt generator while reusing its object association prediction.

DAVIS

Note: Using ViT-L backbone. We adopt the SOTA model XMem as our video boxes prompt generator while reusing its object association prediction.

Interactive segmentation comparison using various points

Note:Using ViT-L backbone. On the high-quality COIFT (zero-shot) and DIS val set.

Citation

If you find HQ-SAM useful in your research or refer to the provided baseline results, please star ⭐ this repository and consider citing 📝:

@article{sam_hq,
    title={Segment Anything in High Quality},
    author={Ke, Lei and Ye, Mingqiao and Danelljan, Martin and Liu, Yifan and Tai, Yu-Wing and Tang, Chi-Keung and Yu, Fisher},
    journal = {arXiv:2306.01567},
    year = {2023}
}  

Acknowledgments

• Thanks SAM for their public code and released models.