Reformat project links (#2767)

docs/projects.md

@@ -8,22 +8,21 @@ Pull requests are also welcomed.
 To make this list also a reference for the community to develop and compare new object detection algorithms, we list them following the time order of top-tier conferences.
 Methods already supported and maintained by MMDetection are not listed.
 
-- [SEPC](https://github.com/jshilong/SEPC): [Scale-equalizing Pyramid Convolution for Object Detection](https://arxiv.org/abs/2005.03101), CVPR2020.
-- [TSD](https://github.com/Sense-X/TSD): [Revisiting the Sibling Head in Object Detector](https://arxiv.org/abs/2003.07540), CVPR2020.
-- [PolarMask](https://github.com/xieenze/PolarMask): [Single Shot Instance Segmentation with Polar Representation](https://arxiv.org/abs/1909.13226), CVPR2020.
-- [Hit-Detector](https://github.com/ggjy/HitDet.pytorch): [Hierarchical Trinity Architecture Search for Object Detection](https://arxiv.org/abs/2003.11818), CVPR2020.
-- [ZeroQ](https://github.com/amirgholami/ZeroQ): [A Novel Zero Shot Quantization Framework](https://arxiv.org/abs/2001.00281), CVPR2020.
+- Scale-equalizing Pyramid Convolution for Object Detection, CVPR2020. [[paper]](https://arxiv.org/abs/2005.03101) [[github]](https://github.com/jshilong/SEPC)
+- Revisiting the Sibling Head in Object Detector, CVPR2020. [[paper]](https://arxiv.org/abs/2003.07540)[[github]](https://github.com/Sense-X/TSD)
+- PolarMask: Single Shot Instance Segmentation with Polar Representation, CVPR2020. [[paper]](https://arxiv.org/abs/1909.13226)[[github]](https://github.com/xieenze/PolarMask)
+- Hit-Detector: Hierarchical Trinity Architecture Search for Object Detection, CVPR2020. [[paper]](https://arxiv.org/abs/2003.11818)[[github]](https://github.com/ggjy/HitDet.pytorch)
+- ZeroQ: A Novel Zero Shot Quantization Framework, CVPR2020. [[paper]](https://arxiv.org/abs/2001.00281)[[github]](https://github.com/amirgholami/ZeroQ)
+- CBNet: A Novel Composite Backbone Network Architecture for Object Detection, AAAI2020. [[paper]](https://aaai.org/Papers/AAAI/2020GB/AAAI-LiuY.1833.pdf)[[github]](https://github.com/VDIGPKU/CBNet)
+- RDSNet: A New Deep Architecture for Reciprocal Object Detection and Instance Segmentation, AAAI2020. [[paper]](https://arxiv.org/abs/1912.05070)[[github]](https://github.com/wangsr126/RDSNet)
+- Training-Time-Friendly Network for Real-Time Object Detection, AAAI2020. [[paper]](https://arxiv.org/abs/1909.00700)[[github]](https://github.com/ZJULearning/ttfnet)
 
-- [CBNet](https://github.com/VDIGPKU/CBNet): [A Novel Composite Backbone Network Architecture for Object Detection](https://aaai.org/Papers/AAAI/2020GB/AAAI-LiuY.1833.pdf), AAAI2020
-- [RDSNet](https://github.com/wangsr126/RDSNet): [A New Deep Architecture for Reciprocal Object Detection and Instance Segmentation](https://arxiv.org/abs/1912.05070), AAAI2020.
-- [TTFNet](https://github.com/ZJULearning/ttfnet): [Training-Time-Friendly Network for Real-Time Object Detection](https://arxiv.org/abs/1909.00700), AAAI2020.
+- Cascade RPN: Delving into High-Quality Region Proposal Network with Adaptive Convolution, NeurIPS 2019. [[paper]](https://arxiv.org/abs/1909.06720)[[github]](https://github.com/thangvubk/Cascade-RPN)
+- Reasoning R-CNN: Unifying Adaptive Global Reasoning into Large-scale Object Detection, CVPR2019. [[paper]](http://openaccess.thecvf.com/content_CVPR_2019/papers/Xu_Reasoning-RCNN_Unifying_Adaptive_Global_Reasoning_Into_Large-Scale_Object_Detection_CVPR_2019_paper.pdf)[[github]](https://github.com/chanyn/Reasoning-RCNN)
+- Learning RoI Transformer for Oriented Object Detection in Aerial Images, CVPR2019. [[paper]](https://arxiv.org/abs/1812.00155)[[github]](https://github.com/dingjiansw101/AerialDetection)
 
-- [Cascade RPN](https://github.com/thangvubk/Cascade-RPN): [Delving into High-Quality Region Proposal Network with Adaptive Convolution](https://arxiv.org/abs/1909.06720), NeurIPS 2019.
-- [Reasoning R-CNN](https://github.com/chanyn/Reasoning-RCNN): [Unifying Adaptive Global Reasoning into Large-scale Object Detection](http://openaccess.thecvf.com/content_CVPR_2019/papers/Xu_Reasoning-RCNN_Unifying_Adaptive_Global_Reasoning_Into_Large-Scale_Object_Detection_CVPR_2019_paper.pdf), CVPR2019.
-- [DOTA](https://github.com/dingjiansw101/AerialDetection): [Learning RoI Transformer for Oriented Object Detection in Aerial Images](https://arxiv.org/abs/1812.00155), CVPR2019.
-
-- [SOLO](https://github.com/WXinlong/SOLO): [Segmenting Objects by Locations](https://arxiv.org/abs/1912.04488)
-- [SOLOv2](https://github.com/WXinlong/SOLO): [Dynamic, Faster and Stronger](https://arxiv.org/abs/2003.10152)
-- [Dense Peppoints](https://github.com/justimyhxu/Dense-RepPoints): [Representing Visual Objects with Dense Point Sets](https://arxiv.org/abs/1912.11473).
-- [IterDet](https://github.com/saic-vul/iterdet): [Iterative Scheme for Object Detection in Crowded Environments](https://arxiv.org/abs/2005.05708)
-- [CBN](https://github.com/Howal/Cross-iterationBatchNorm): [Cross-Iteration Batch Normalization](https://arxiv.org/abs/2002.05712)
+- SOLO: Segmenting Objects by Locations. [[paper]](https://arxiv.org/abs/1912.04488)[[github]](https://github.com/WXinlong/SOLO)
+- SOLOv2: Dynamic, Faster and Stronger. [[paper]](https://arxiv.org/abs/2003.10152)[[github]](https://github.com/WXinlong/SOLO)
+- Dense Peppoints: Representing Visual Objects with Dense Point Sets. [[paper]](https://arxiv.org/abs/1912.11473)[[github]](https://github.com/justimyhxu/Dense-RepPoints)
+- IterDet: Iterative Scheme for Object Detection in Crowded Environments. [[paper]](https://arxiv.org/abs/2005.05708)[[github]](https://github.com/saic-vul/iterdet)
+- Cross-Iteration Batch Normalization [[paper]](https://arxiv.org/abs/2002.05712)[[github]](https://github.com/Howal/Cross-iterationBatchNorm)