# GLEE: General Object Foundation Model for Images and Videos at Scale
> #### Junfeng Wu\*, Yi Jiang\*, Qihao Liu, Zehuan Yuan, Xiang Bai†,and Song Bai†
>
> \* Equal Contribution, †Correspondence
\[[Project Page](https://glee-vision.github.io/)\] \[[Paper](https://arxiv.org/abs/2312.09158)\] \[[HuggingFace Demo](https://huggingface.co/spaces/Junfeng5/GLEE_demo)\] \[[Video Demo](https://youtu.be/PSVhfTPx0GQ)\]
[](https://paperswithcode.com/sota/long-tail-video-object-segmentation-on-burst-1?p=general-object-foundation-model-for-images)[](https://paperswithcode.com/sota/video-instance-segmentation-on-ovis-1?p=general-object-foundation-model-for-images)[](https://paperswithcode.com/sota/referring-video-object-segmentation-on-refer?p=general-object-foundation-model-for-images)[](https://paperswithcode.com/sota/referring-expression-segmentation-on-refer-1?p=general-object-foundation-model-for-images)[](https://paperswithcode.com/sota/multi-object-tracking-on-tao?p=general-object-foundation-model-for-images)[](https://paperswithcode.com/sota/open-world-instance-segmentation-on-uvo?p=general-object-foundation-model-for-images)[](https://paperswithcode.com/sota/referring-expression-segmentation-on-refcoco?p=general-object-foundation-model-for-images)[](https://paperswithcode.com/sota/referring-expression-segmentation-on-refcocog?p=general-object-foundation-model-for-images)[](https://paperswithcode.com/sota/video-instance-segmentation-on-youtube-vis-1?p=general-object-foundation-model-for-images)[](https://paperswithcode.com/sota/object-detection-on-lvis-v1-0-val?p=general-object-foundation-model-for-images)[](https://paperswithcode.com/sota/instance-segmentation-on-lvis-v1-0-val?p=general-object-foundation-model-for-images)[](https://paperswithcode.com/sota/referring-expression-comprehension-on-refcoco?p=general-object-foundation-model-for-images)[](https://paperswithcode.com/sota/referring-expression-segmentation-on-refcoco-3?p=general-object-foundation-model-for-images)[](https://paperswithcode.com/sota/instance-segmentation-on-coco-minival?p=general-object-foundation-model-for-images)[](https://paperswithcode.com/sota/referring-expression-comprehension-on?p=general-object-foundation-model-for-images)[](https://paperswithcode.com/sota/instance-segmentation-on-coco?p=general-object-foundation-model-for-images)[](https://paperswithcode.com/sota/referring-expression-comprehension-on-refcoco-1?p=general-object-foundation-model-for-images)
## Highlight:
- GLEE is accepted by **CVPR2024** as **Highlight**!
- GLEE is a general object foundation model jointly trained on over **ten million images** from various benchmarks with diverse levels of supervision.
- GLEE is capable of addressing **a wide range of object-centric tasks** simultaneously while maintaining **SOTA** performance.
- GLEE demonstrates remarkable versatility and robust **zero-shot transferability** across a spectrum of object-level image and video tasks, and able to **serve as a foundational component** for enhancing other architectures or models.
We will release the following contents for **GLEE**:exclamation:
- [x] Demo Code
- [x] Model Zoo
- [x] Comprehensive User Guide
- [x] Training Code and Scripts
- [ ] Detailed Evaluation Code and Scripts
- [ ] Tutorial for Zero-shot Testing or Fine-tuning GLEE on New Datasets
## Getting started
1. Installation: Please refer to [INSTALL.md](assets/INSTALL.md) for more details.
2. Data preparation: Please refer to [DATA.md](assets/DATA.md) for more details.
3. Training: Please refer to [TRAIN.md](assets/TRAIN.md) for more details.
4. Testing: Please refer to [TEST.md](assets/TEST.md) for more details.
5. Model zoo: Please refer to [MODEL_ZOO.md](assets/MODEL_ZOO.md) for more details.
## Run the demo APP
Try our online demo app on \[[HuggingFace Demo](https://huggingface.co/spaces/Junfeng5/GLEE_demo)\] or use it locally:
```bash
git clone https://github.com/FoundationVision/GLEE
# support CPU and GPU running
python app.py
```
# Introduction
GLEE has been trained on over ten million images from 16 datasets, fully harnessing both existing annotated data and cost-effective automatically labeled data to construct a diverse training set. This extensive training regime endows GLEE with formidable generalization capabilities.
GLEE consists of an image encoder, a text encoder, a visual prompter, and an object decoder, as illustrated in Figure. The text encoder processes arbitrary descriptions related to the task, including **1) object category list 2）object names in any form 3）captions about objects 4）referring expressions**. The visual prompter encodes user inputs such as **1) points 2) bounding boxes 3) scribbles** during interactive segmentation into corresponding visual representations of target objects. Then they are integrated into a detector for extracting objects from images according to textual and visual input.
Based on the above designs, GLEE can be used to seamlessly unify a wide range of object perception tasks in images and videos, including object detection, instance segmentation, grounding, multi-target tracking (MOT), video instance segmentation (VIS), video object segmentation (VOS), interactive segmentation and tracking, and supports **open-world/large-vocabulary image and video detection and segmentation** tasks.
# Results
## Image-level tasks
## Video-level tasks
`
# Citing GLEE
```
@misc{wu2023GLEE,
author= {Junfeng Wu, Yi Jiang, Qihao Liu, Zehuan Yuan, Xiang Bai, Song Bai},
title = {General Object Foundation Model for Images and Videos at Scale},
year={2023},
eprint={2312.09158},
archivePrefix={arXiv}
}
```
## Acknowledgments
- Thanks [UNINEXT](https://github.com/MasterBin-IIAU/UNINEXT) for the implementation of multi-dataset training and data processing.
- Thanks [VNext](https://github.com/wjf5203/VNext) for providing experience of Video Instance Segmentation (VIS).
- Thanks [SEEM](https://github.com/UX-Decoder/Segment-Everything-Everywhere-All-At-Once) for providing the implementation of the visual prompter.
- Thanks [MaskDINO](https://github.com/IDEA-Research/MaskDINO) for providing a powerful detector and segmenter.