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README.md

@@ -1,142 +1,25 @@
 
 # Learning Open-World Object Proposals without Learning to Classify
 
-## Pytorch implementation for "Learning Open-World Object Proposals without Learning to Classify" (arXiv 2021) <br/>[[Paper](https://arxiv.org/abs/2108.06753)]
+## Pytorch implementation for "Learning Open-World Object Proposals without Learning to Classify" ([arXiv 2021](https://arxiv.org/abs/2108.06753)) <br/>
 
 [Dahun Kim](https://mcahny.github.io/), [Tsung-Yi Lin](https://scholar.google.com/citations?user=_BPdgV0AAAAJ), [Anelia Angelova](https://scholar.google.co.kr/citations?user=nkmDOPgAAAAJ), [In So Kweon](https://rcv.kaist.ac.kr), and [Weicheng Kuo](https://weichengkuo.github.io/).
 
 
-<img src="./images/epic.png" width="800"> <br/>
-**OLN: Object Localization Network**
-Humans can recognize novel objects in this image despite having never seen them  before. “Is it possible to learn open-world (novel) object proposals?” In this paper we propose Object Localization Network (OLN) that learns localization cues instead of foreground vs background classification. Only trained on COCO, OLN is able to propose many novel objects (top) missed by Mask R-CNN (bottom) on an out-of-sample frame in an ego-centric video.
-
-
-
-**News**: We released the technical report on [ArXiv](https://arxiv.org/abs/1906.07155).
-
-Documentation: https://mmdetection.readthedocs.io/
-
 ## Introduction
 
-MMDetection is an open source object detection toolbox based on PyTorch. It is
-a part of the OpenMMLab project developed by [Multimedia Laboratory, CUHK](http://mmlab.ie.cuhk.edu.hk/).
+Humans can recognize novel objects in this image despite having never seen them  before. “Is it possible to learn open-world (novel) object proposals?” In this paper we propose **Object Localization Network (OLN)** that learns localization cues instead of foreground vs background classification. Only trained on COCO, OLN is able to propose many novel objects (top) missed by Mask R-CNN (bottom) on an out-of-sample frame in an ego-centric video.
 
-The master branch works with **PyTorch 1.3 to 1.6**.
-The old v1.x branch works with PyTorch 1.1 to 1.4, but v2.0 is strongly recommended for faster speed, higher performance, better design and more friendly usage.
-
-![demo image](resources/coco_test_12510.jpg)
-
-### Major features
-
-- **Modular Design**
-
-  We decompose the detection framework into different components and one can easily construct a customized object detection framework by combining different modules.
-
-- **Support of multiple frameworks out of box**
-
-  The toolbox directly supports popular and contemporary detection frameworks, *e.g.* Faster RCNN, Mask RCNN, RetinaNet, etc.
-
-- **High efficiency**
-
-  All basic bbox and mask operations run on GPUs. The training speed is faster than or comparable to other codebases, including [Detectron2](https://github.com/facebookresearch/detectron2), [maskrcnn-benchmark](https://github.com/facebookresearch/maskrcnn-benchmark) and [SimpleDet](https://github.com/TuSimple/simpledet).
-
-- **State of the art**
-
-  The toolbox stems from the codebase developed by the *MMDet* team, who won [COCO Detection Challenge](http://cocodataset.org/#detection-leaderboard) in 2018, and we keep pushing it forward.
-
-Apart from MMDetection, we also released a library [mmcv](https://github.com/open-mmlab/mmcv) for computer vision research, which is heavily depended on by this toolbox.
+<img src="./images/epic.png" width="300"> <img src="./images/oln_overview.png" width="600"> <br/>
 
 ## License
 
 This project is released under the [Apache 2.0 license](LICENSE).
 
-## Changelog
-
-v2.8.0 was released in 04/01/2021.
-Please refer to [changelog.md](docs/changelog.md) for details and release history.
-A comparison between v1.x and v2.0 codebases can be found in [compatibility.md](docs/compatibility.md).
-
-## Benchmark and model zoo
-
-Results and models are available in the [model zoo](docs/model_zoo.md).
-
-Supported backbones:
-
-- [x] ResNet
-- [x] ResNeXt
-- [x] VGG
-- [x] HRNet
-- [x] RegNet
-- [x] Res2Net
-- [x] ResNeSt
-
-Supported methods:
-
-- [x] [RPN](configs/rpn)
-- [x] [Fast R-CNN](configs/fast_rcnn)
-- [x] [Faster R-CNN](configs/faster_rcnn)
-- [x] [Mask R-CNN](configs/mask_rcnn)
-- [x] [Cascade R-CNN](configs/cascade_rcnn)
-- [x] [Cascade Mask R-CNN](configs/cascade_rcnn)
-- [x] [SSD](configs/ssd)
-- [x] [RetinaNet](configs/retinanet)
-- [x] [GHM](configs/ghm)
-- [x] [Mask Scoring R-CNN](configs/ms_rcnn)
-- [x] [Double-Head R-CNN](configs/double_heads)
-- [x] [Hybrid Task Cascade](configs/htc)
-- [x] [Libra R-CNN](configs/libra_rcnn)
-- [x] [Guided Anchoring](configs/guided_anchoring)
-- [x] [FCOS](configs/fcos)
-- [x] [RepPoints](configs/reppoints)
-- [x] [Foveabox](configs/foveabox)
-- [x] [FreeAnchor](configs/free_anchor)
-- [x] [NAS-FPN](configs/nas_fpn)
-- [x] [ATSS](configs/atss)
-- [x] [FSAF](configs/fsaf)
-- [x] [PAFPN](configs/pafpn)
-- [x] [Dynamic R-CNN](configs/dynamic_rcnn)
-- [x] [PointRend](configs/point_rend)
-- [x] [CARAFE](configs/carafe/README.md)
-- [x] [DCNv2](configs/dcn/README.md)
-- [x] [Group Normalization](configs/gn/README.md)
-- [x] [Weight Standardization](configs/gn+ws/README.md)
-- [x] [OHEM](configs/faster_rcnn/faster_rcnn_r50_fpn_ohem_1x_coco.py)
-- [x] [Soft-NMS](configs/faster_rcnn/faster_rcnn_r50_fpn_soft_nms_1x_coco.py)
-- [x] [Generalized Attention](configs/empirical_attention/README.md)
-- [x] [GCNet](configs/gcnet/README.md)
-- [x] [Mixed Precision (FP16) Training](configs/fp16/README.md)
-- [x] [InstaBoost](configs/instaboost/README.md)
-- [x] [GRoIE](configs/groie/README.md)
-- [x] [DetectoRS](configs/detectors/README.md)
-- [x] [Generalized Focal Loss](configs/gfl/README.md)
-- [x] [CornerNet](configs/cornernet/README.md)
-- [x] [Side-Aware Boundary Localization](configs/sabl/README.md)
-- [x] [YOLOv3](configs/yolo/README.md)
-- [x] [PAA](configs/paa/README.md)
-- [x] [YOLACT](configs/yolact/README.md)
-- [x] [CentripetalNet](configs/centripetalnet/README.md)
-- [x] [VFNet](configs/vfnet/README.md)
-- [x] [DETR](configs/detr/README.md)
-- [x] [CascadeRPN](configs/cascade_rpn/README.md)
-
-Some other methods are also supported in [projects using MMDetection](./docs/projects.md).
-
 ## Installation
 
 Please refer to [get_started.md](docs/get_started.md) for installation.
 
-## Getting Started
-
-Please see [get_started.md](docs/get_started.md) for the basic usage of MMDetection.
-We provide [colab tutorial](demo/MMDet_Tutorial.ipynb), and full guidance for quick run [with existing dataset](docs/1_exist_data_model.md) and [with new dataset](docs/2_new_data_model.md) for beginners.
-There are also tutorials for [finetuning models](docs/tutorials/finetune.md), [adding new dataset](docs/tutorials/new_dataset.md), [designing data pipeline](docs/tutorials/data_pipeline.md), [customizing models](docs/tutorials/customize_models.md), [customizing runtime settings](docs/tutorials/customize_runtime.md) and [useful tools](docs/useful_tools.md).
-
-Please refer to [FAQ](docs/faq.md) for frequently asked questions.
-
-## Contributing
-
-We appreciate all contributions to improve MMDetection. Please refer to [CONTRIBUTING.md](.github/CONTRIBUTING.md) for the contributing guideline.
-
 ## Acknowledgement
 
 MMDetection is an open source project that is contributed by researchers and engineers from various colleges and companies. We appreciate all the contributors who implement their methods or add new features, as well as users who give valuable feedbacks.
@@ -147,27 +30,10 @@ We wish that the toolbox and benchmark could serve the growing research communit
 If you use this toolbox or benchmark in your research, please cite this project.
 
 ```
-@article{mmdetection,
-  title   = {{MMDetection}: Open MMLab Detection Toolbox and Benchmark},
-  author  = {Chen, Kai and Wang, Jiaqi and Pang, Jiangmiao and Cao, Yuhang and
-             Xiong, Yu and Li, Xiaoxiao and Sun, Shuyang and Feng, Wansen and
-             Liu, Ziwei and Xu, Jiarui and Zhang, Zheng and Cheng, Dazhi and
-             Zhu, Chenchen and Cheng, Tianheng and Zhao, Qijie and Li, Buyu and
-             Lu, Xin and Zhu, Rui and Wu, Yue and Dai, Jifeng and Wang, Jingdong
-             and Shi, Jianping and Ouyang, Wanli and Loy, Chen Change and Lin, Dahua},
-  journal= {arXiv preprint arXiv:1906.07155},
-  year={2019}
+@article{kim2021oln,
+  title={Learning Open-World Object Proposals without Learning to Classify},
+  author={Kim, Dahun and Lin, Tsung-Yi and Angelova, Anelia and Kweon, In So and Kuo, Weicheng},
+  journal={arXiv preprint arXiv:2108.06753},
+  year={2021}
 }
 ```
-
-## Projects in OpenMMLab
-
-- [MMCV](https://github.com/open-mmlab/mmcv): OpenMMLab foundational library for computer vision.
-- [MMClassification](https://github.com/open-mmlab/mmclassification): OpenMMLab image classification toolbox and benchmark.
-- [MMDetection](https://github.com/open-mmlab/mmdetection): OpenMMLab detection toolbox and benchmark.
-- [MMDetection3D](https://github.com/open-mmlab/mmdetection3d): OpenMMLab's next-generation platform for general 3D object detection.
-- [MMSegmentation](https://github.com/open-mmlab/mmsegmentation): OpenMMLab semantic segmentation toolbox and benchmark.
-- [MMAction2](https://github.com/open-mmlab/mmaction2): OpenMMLab's next-generation action understanding toolbox and benchmark.
-- [MMTracking](https://github.com/open-mmlab/mmtracking): OpenMMLab video perception toolbox and benchmark.
-- [MMPose](https://github.com/open-mmlab/mmpose): OpenMMLab pose estimation toolbox and benchmark.
-- [MMEditing](https://github.com/open-mmlab/mmediting): OpenMMLab image and video editing toolbox.