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COCO + LVIS
JOINT COCO AND MAPILLARY WORKSHOP AT ICCV 2019: COCO Joint COCO and Mapillary Workshop at ICCV 2019: COCO Keypoint Challenge Track Technical Report: Res-Steps-Net for Multi-Person Pose Estimation Yuanhao Cai 1;2 Zhicheng Wang Binyi Yin1;3 Ruihao Yin 3 Angang Du 4 Zhengxiong Luo 1;5 Zeming Li Xinyu Zhou 1Gang Yu Erjin Zhou Xiangyu Zhang 1Yichen Wei Jian Sun1 1Megvii Inc. 2Tsinghua University 3Beihang University 4Ocean University of China JOINT COCO AND MAPILLARY WORKSHOP AT ICCV 2019: PANOPTIC PAM MCB Huge Backbone mIOU 49.3 X 49.9 X X 50.4 X X X 53.9 Table 1: Ablation study on COCO validation set. PAM rep-resents Parallel Attention Module, MCB represents Multi- A UNIFIED ARCHITECTURE FOR INSTANCE AND SEMANTIC SEGMENTATION FPN Architecture 1 4 1 8 1 16 1 32 image 1 2x up 1x1 conv + high resolution low resolution strong features strong features He, K., Zhang, X., Ren, S., & Sun, J. MSCOCO KEYPOINTS CHALLENGE 2018 Results on COCO 2018 test_challenge test_dev mini_validation Ensemble model 76.4(final submission) 78.1 80.0 Single model - 77.1 79.0 Year2016 2017 2018
FPN-BASEDNETWORKFOR PANOPTIC SEGMENTATION FPN-basedNetworkfor Panoptic Segmentation CaribbeanTeam Yanwei Li* 1,2, NaiyuGao*1,2, ChaoxuGuo1,2, XinzeChen1, QianZhang1, Guan Huang1, Xin Zhao2, KaiqiHuang2 TEAM OKS KEYPOINTS DETECTION Team OKS Keypoints Detection Yujie Wang 1*, Changbao Wang , Quanquan Li2*, Biao Leng 1, Zhoujun Li , Junjie Yan2 1Beihang University 2Sensetime Group Limited (*Equal contribution. This work was done when Yujie Wang and Changbao Wang were interns at Sensetime Group Limited) MSCOCO INSTANCE SEGMENTATION CHALLENGES 2018 Overview 37.4 41.6 52.6 56.0 35 40 45 50 55 60 2015 2016 2017(Megvii) Ours Detector mmAP 28.4 37.6 46.7 48.8 25 30 35 40 45 50 55 2015 2016 2017 Ours Mask mmAP Object Detector COCO CHALLENGE 2018 PANOPTIC SEGMENTATION TASK COCO Challenge 2018 Panoptic Segmentation Task Team name: PKU_360 Team members: Yibo Yang, Xia Li, Hongyang Li, Tiancheng Shen, Zhouchen Lin, Jian Dong, Jiashi Feng, Shuicheng Yan COCO - COMMON OBJECTS IN CONTEXT info@cocodataset.org. Home; PeopleCOCO + LVIS
JOINT COCO AND MAPILLARY WORKSHOP AT ICCV 2019: COCO Joint COCO and Mapillary Workshop at ICCV 2019: COCO Keypoint Challenge Track Technical Report: Res-Steps-Net for Multi-Person Pose Estimation Yuanhao Cai 1;2 Zhicheng Wang Binyi Yin1;3 Ruihao Yin 3 Angang Du 4 Zhengxiong Luo 1;5 Zeming Li Xinyu Zhou 1Gang Yu Erjin Zhou Xiangyu Zhang 1Yichen Wei Jian Sun1 1Megvii Inc. 2Tsinghua University 3Beihang University 4Ocean University of China JOINT COCO AND MAPILLARY WORKSHOP AT ICCV 2019: PANOPTIC PAM MCB Huge Backbone mIOU 49.3 X 49.9 X X 50.4 X X X 53.9 Table 1: Ablation study on COCO validation set. PAM rep-resents Parallel Attention Module, MCB represents Multi- A UNIFIED ARCHITECTURE FOR INSTANCE AND SEMANTIC SEGMENTATION FPN Architecture 1 4 1 8 1 16 1 32 image 1 2x up 1x1 conv + high resolution low resolution strong features strong features He, K., Zhang, X., Ren, S., & Sun, J. MSCOCO KEYPOINTS CHALLENGE 2018 Results on COCO 2018 test_challenge test_dev mini_validation Ensemble model 76.4(final submission) 78.1 80.0 Single model - 77.1 79.0 Year2016 2017 2018
FPN-BASEDNETWORKFOR PANOPTIC SEGMENTATION FPN-basedNetworkfor Panoptic Segmentation CaribbeanTeam Yanwei Li* 1,2, NaiyuGao*1,2, ChaoxuGuo1,2, XinzeChen1, QianZhang1, Guan Huang1, Xin Zhao2, KaiqiHuang2 TEAM OKS KEYPOINTS DETECTION Team OKS Keypoints Detection Yujie Wang 1*, Changbao Wang , Quanquan Li2*, Biao Leng 1, Zhoujun Li , Junjie Yan2 1Beihang University 2Sensetime Group Limited (*Equal contribution. This work was done when Yujie Wang and Changbao Wang were interns at Sensetime Group Limited) MSCOCO INSTANCE SEGMENTATION CHALLENGES 2018 Overview 37.4 41.6 52.6 56.0 35 40 45 50 55 60 2015 2016 2017(Megvii) Ours Detector mmAP 28.4 37.6 46.7 48.8 25 30 35 40 45 50 55 2015 2016 2017 Ours Mask mmAP Object Detector COCO CHALLENGE 2018 PANOPTIC SEGMENTATION TASK COCO Challenge 2018 Panoptic Segmentation Task Team name: PKU_360 Team members: Yibo Yang, Xia Li, Hongyang Li, Tiancheng Shen, Zhouchen Lin, Jian Dong, Jiashi Feng, Shuicheng Yan PRESENTATIONS.COCODATASET.ORG presentations.cocodataset.org 1000 false COCO17-Detect-MSRA.pdf 2020-04-01T21:24:36.000Z "2275ddc26162e380c31cf16ce39f0045" 1956170 COCO + MAPILLARY 2018 3. COCO Challenges. COCO is an image dataset designed to spur object detection research with a focus on detecting objects in context. The annotations include instance segmentations for object belonging to 80 categories, stuff segmentations for 91 categories, keypoint annotations for person instances, and five image captions per image. JOINT COCO AND MAPILLARY WORKSHOP AT ICCV 2019: COCO Feature Mask Label BBox Method PQ Th SQ Th RQ Th Heuristic Fusion 54.8 83.9 57.6 3 SM 56.9 84.1 67.2 3 SHR 56.9 84.1 67.2 3 3 3 SRM57.1 83.6 67.8
JOINT COCO AND MAPILLARY WORKSHOP AT ICCV 2019: COCO Joint COCO and Mapillary Workshop at ICCV 2019: COCO Keypoint Detection Challenge Track Technical Report: ByteDance HRNet Bin Xiao, Zaizhou Gong, Yifan Lu, Linfu Wen COCO CHALLENGE 2018 PANOPTIC SEGMENTATION TASK COCO Challenge 2018 Panoptic Segmentation Task Team name: PKU_360 Team members: Yibo Yang, Xia Li, Hongyang Li, Tiancheng Shen, Zhouchen Lin, Jian Dong, Jiashi Feng, Shuicheng Yan JOINT COCO AND MAPILLARY RECOGNITION CHALLENGE WORKSHOP 1 Joint COCO and Mapillary Recognition Challenge Workshop Sunday, September 9th, ECCV 2018 Alexander Kirillov, Facebook AI Research 2018Panoptic Challenge
DEFORMABLE CONVOLUTIONAL NETWORKS XCeption -> Aligned XCeption 14x14x728 feature maps separable conv 728, 3x3, pad 1 separable conv 1024, 3x3, pad 1 max pool 3x3, stride2,pad 1 conv 1024 1x1
MATTEO RUGGERO RONCHI Matteo Ruggero Ronchi COCO and Places Visual Recognition Challenges Workshop Sunday, October 29th, Venice, Italy 2017 Keypoints Challenge PANOPTIC SEGMENTATION: UNIFYING SEMANTIC AND INSTANCE Unifying Semantic and Instance Segmentation Semantic Segmentation Object Detection/Seg • per-pixel annotation • simple accuracy measure • instances indistinguishable LEARNING EFFICIENT DENSEPOSENETWORK 1 Learning Efficient DensePoseNetwork--ECCV 2018 Spotlight YuchenMa, XinzeChen, Guan Huang HorizonRobotics COCO - COMMON OBJECTS IN CONTEXT info@cocodataset.org. Home; PeopleCOCO + LVIS
COCO + MAPILLARY
JOINT COCO AND MAPILLARY WORKSHOP AT ICCV 2019: COCO Joint COCO and Mapillary Workshop at ICCV 2019: COCO Keypoint Challenge Track Technical Report: Res-Steps-Net for Multi-Person Pose Estimation Yuanhao Cai 1;2 Zhicheng Wang Binyi Yin1;3 Ruihao Yin 3 Angang Du 4 Zhengxiong Luo 1;5 Zeming Li Xinyu Zhou 1Gang Yu Erjin Zhou Xiangyu Zhang 1Yichen Wei Jian Sun1 1Megvii Inc. 2Tsinghua University 3Beihang University 4Ocean University of China FPN-BASEDNETWORKFOR PANOPTIC SEGMENTATION FPN-basedNetworkfor Panoptic Segmentation CaribbeanTeam Yanwei Li* 1,2, NaiyuGao*1,2, ChaoxuGuo1,2, XinzeChen1, QianZhang1, Guan Huang1, Xin Zhao2, KaiqiHuang2 MSCOCO KEYPOINTS CHALLENGE 2018 Results on COCO 2018 test_challenge test_dev mini_validation Ensemble model 76.4(final submission) 78.1 80.0 Single model - 77.1 79.0 Year2016 2017 2018
MSCOCO INSTANCE SEGMENTATION CHALLENGES 2018 Overview 37.4 41.6 52.6 56.0 35 40 45 50 55 60 2015 2016 2017(Megvii) Ours Detector mmAP 28.4 37.6 46.7 48.8 25 30 35 40 45 50 55 2015 2016 2017 Ours Mask mmAP Object Detector COCO CHALLENGE 2018 PANOPTIC SEGMENTATION TASK COCO Challenge 2018 Panoptic Segmentation Task Team name: PKU_360 Team members: Yibo Yang, Xia Li, Hongyang Li, Tiancheng Shen, Zhouchen Lin, Jian Dong, Jiashi Feng, Shuicheng Yan 2018 KEYPOINTS CHALLENGE / 24 Multiple Perspectives, Instances, Sizes, Occlusions: 3 COCO Keypoints Dataset (I) • 17 types of keypoints. • 58,945 images. • 156,165 annotated people. TEAM OKS KEYPOINTS DETECTION Team OKS Keypoints Detection Yujie Wang 1*, Changbao Wang , Quanquan Li2*, Biao Leng 1, Zhoujun Li , Junjie Yan2 1Beihang University 2Sensetime Group Limited (*Equal contribution. This work was done when Yujie Wang and Changbao Wang were interns at Sensetime Group Limited) COCO - COMMON OBJECTS IN CONTEXT info@cocodataset.org. Home; PeopleCOCO + LVIS
COCO + MAPILLARY
JOINT COCO AND MAPILLARY WORKSHOP AT ICCV 2019: COCO Joint COCO and Mapillary Workshop at ICCV 2019: COCO Keypoint Challenge Track Technical Report: Res-Steps-Net for Multi-Person Pose Estimation Yuanhao Cai 1;2 Zhicheng Wang Binyi Yin1;3 Ruihao Yin 3 Angang Du 4 Zhengxiong Luo 1;5 Zeming Li Xinyu Zhou 1Gang Yu Erjin Zhou Xiangyu Zhang 1Yichen Wei Jian Sun1 1Megvii Inc. 2Tsinghua University 3Beihang University 4Ocean University of China FPN-BASEDNETWORKFOR PANOPTIC SEGMENTATION FPN-basedNetworkfor Panoptic Segmentation CaribbeanTeam Yanwei Li* 1,2, NaiyuGao*1,2, ChaoxuGuo1,2, XinzeChen1, QianZhang1, Guan Huang1, Xin Zhao2, KaiqiHuang2 MSCOCO KEYPOINTS CHALLENGE 2018 Results on COCO 2018 test_challenge test_dev mini_validation Ensemble model 76.4(final submission) 78.1 80.0 Single model - 77.1 79.0 Year2016 2017 2018
MSCOCO INSTANCE SEGMENTATION CHALLENGES 2018 Overview 37.4 41.6 52.6 56.0 35 40 45 50 55 60 2015 2016 2017(Megvii) Ours Detector mmAP 28.4 37.6 46.7 48.8 25 30 35 40 45 50 55 2015 2016 2017 Ours Mask mmAP Object Detector COCO CHALLENGE 2018 PANOPTIC SEGMENTATION TASK COCO Challenge 2018 Panoptic Segmentation Task Team name: PKU_360 Team members: Yibo Yang, Xia Li, Hongyang Li, Tiancheng Shen, Zhouchen Lin, Jian Dong, Jiashi Feng, Shuicheng Yan 2018 KEYPOINTS CHALLENGE / 24 Multiple Perspectives, Instances, Sizes, Occlusions: 3 COCO Keypoints Dataset (I) • 17 types of keypoints. • 58,945 images. • 156,165 annotated people. TEAM OKS KEYPOINTS DETECTION Team OKS Keypoints Detection Yujie Wang 1*, Changbao Wang , Quanquan Li2*, Biao Leng 1, Zhoujun Li , Junjie Yan2 1Beihang University 2Sensetime Group Limited (*Equal contribution. This work was done when Yujie Wang and Changbao Wang were interns at Sensetime Group Limited) PRESENTATIONS.COCODATASET.ORG presentations.cocodataset.org 1000 false COCO17-Detect-MSRA.pdf 2020-04-01T21:24:36.000Z "2275ddc26162e380c31cf16ce39f0045" 1956170 COCO CHALLENGE 2018 PANOPTIC SEGMENTATION TASK COCO Challenge 2018 Panoptic Segmentation Task Team name: PKU_360 Team members: Yibo Yang, Xia Li, Hongyang Li, Tiancheng Shen, Zhouchen Lin, Jian Dong, Jiashi Feng, Shuicheng Yan JOINT COCO AND MAPILLARY RECOGNITION CHALLENGE WORKSHOP 1 Joint COCO and Mapillary Recognition Challenge Workshop Sunday, September 9th, ECCV 2018 Alexander Kirillov, Facebook AI Research 2018Panoptic Challenge
A UNIFIED ARCHITECTURE FOR INSTANCE AND SEMANTIC SEGMENTATION FPN Architecture 1 4 1 8 1 16 1 32 image 1 2x up 1x1 conv + high resolution low resolution strong features strong features He, K., Zhang, X., Ren, S., & Sun, J. DEFORMABLE CONVOLUTIONAL NETWORKS XCeption -> Aligned XCeption 14x14x728 feature maps separable conv 728, 3x3, pad 1 separable conv 1024, 3x3, pad 1 max pool 3x3, stride2,pad 1 conv 1024 1x1
MATTEO RUGGERO RONCHI Matteo Ruggero Ronchi COCO and Places Visual Recognition Challenges Workshop Sunday, October 29th, Venice, Italy 2017 Keypoints Challenge PANOPTIC SEGMENTATION: UNIFYING SEMANTIC AND INSTANCE Unifying Semantic and Instance Segmentation Semantic Segmentation Object Detection/Seg • per-pixel annotation • simple accuracy measure • instances indistinguishable LEARNING EFFICIENT DENSEPOSENETWORK 1 Learning Efficient DensePoseNetwork--ECCV 2018 Spotlight YuchenMa, XinzeChen, Guan Huang HorizonRobotics MAPILLARY TASKS JOINT COCO AND MAPILLARY RECOGNITION CHALLENGE Mapillary is a platform for extracting map data from street-level imagery using computer vision 350+ million images, 5.5 million km, 30+billion objects
TEAM MSRA KEYPOINTS DETECTION Team MSRA Keypoints Detection Bin Xiao 1, Dianqi Li 2, Ke Sun , Lei Zhang , Jingdong Wang1 1Microsoft Research Asia 2Microsoft COCO - COMMON OBJECTS IN CONTEXT info@cocodataset.org. Home; PeopleCOCO + LVIS
COCO + MAPILLARY
JOINT COCO AND MAPILLARY WORKSHOP AT ICCV 2019: COCO Joint COCO and Mapillary Workshop at ICCV 2019: COCO Keypoint Challenge Track Technical Report: Res-Steps-Net for Multi-Person Pose Estimation Yuanhao Cai 1;2 Zhicheng Wang Binyi Yin1;3 Ruihao Yin 3 Angang Du 4 Zhengxiong Luo 1;5 Zeming Li Xinyu Zhou 1Gang Yu Erjin Zhou Xiangyu Zhang 1Yichen Wei Jian Sun1 1Megvii Inc. 2Tsinghua University 3Beihang University 4Ocean University of China 2018 KEYPOINTS CHALLENGE / 24 Multiple Perspectives, Instances, Sizes, Occlusions: 3 COCO Keypoints Dataset (I) • 17 types of keypoints. • 58,945 images. • 156,165 annotated people. FPN-BASEDNETWORKFOR PANOPTIC SEGMENTATION FPN-basedNetworkfor Panoptic Segmentation CaribbeanTeam Yanwei Li* 1,2, NaiyuGao*1,2, ChaoxuGuo1,2, XinzeChen1, QianZhang1, Guan Huang1, Xin Zhao2, KaiqiHuang2 MSCOCO KEYPOINTS CHALLENGE 2018 Results on COCO 2018 test_challenge test_dev mini_validation Ensemble model 76.4(final submission) 78.1 80.0 Single model - 77.1 79.0 Year2016 2017 2018
MSCOCO INSTANCE SEGMENTATION CHALLENGES 2018 Overview 37.4 41.6 52.6 56.0 35 40 45 50 55 60 2015 2016 2017(Megvii) Ours Detector mmAP 28.4 37.6 46.7 48.8 25 30 35 40 45 50 55 2015 2016 2017 Ours Mask mmAP Object Detector COCO CHALLENGE 2018 PANOPTIC SEGMENTATION TASK COCO Challenge 2018 Panoptic Segmentation Task Team name: PKU_360 Team members: Yibo Yang, Xia Li, Hongyang Li, Tiancheng Shen, Zhouchen Lin, Jian Dong, Jiashi Feng, Shuicheng Yan TEAM OKS KEYPOINTS DETECTION Team OKS Keypoints Detection Yujie Wang 1*, Changbao Wang , Quanquan Li2*, Biao Leng 1, Zhoujun Li , Junjie Yan2 1Beihang University 2Sensetime Group Limited (*Equal contribution. This work was done when Yujie Wang and Changbao Wang were interns at Sensetime Group Limited) COCO - COMMON OBJECTS IN CONTEXT info@cocodataset.org. Home; PeopleCOCO + LVIS
COCO + MAPILLARY
JOINT COCO AND MAPILLARY WORKSHOP AT ICCV 2019: COCO Joint COCO and Mapillary Workshop at ICCV 2019: COCO Keypoint Challenge Track Technical Report: Res-Steps-Net for Multi-Person Pose Estimation Yuanhao Cai 1;2 Zhicheng Wang Binyi Yin1;3 Ruihao Yin 3 Angang Du 4 Zhengxiong Luo 1;5 Zeming Li Xinyu Zhou 1Gang Yu Erjin Zhou Xiangyu Zhang 1Yichen Wei Jian Sun1 1Megvii Inc. 2Tsinghua University 3Beihang University 4Ocean University of China 2018 KEYPOINTS CHALLENGE / 24 Multiple Perspectives, Instances, Sizes, Occlusions: 3 COCO Keypoints Dataset (I) • 17 types of keypoints. • 58,945 images. • 156,165 annotated people. FPN-BASEDNETWORKFOR PANOPTIC SEGMENTATION FPN-basedNetworkfor Panoptic Segmentation CaribbeanTeam Yanwei Li* 1,2, NaiyuGao*1,2, ChaoxuGuo1,2, XinzeChen1, QianZhang1, Guan Huang1, Xin Zhao2, KaiqiHuang2 MSCOCO KEYPOINTS CHALLENGE 2018 Results on COCO 2018 test_challenge test_dev mini_validation Ensemble model 76.4(final submission) 78.1 80.0 Single model - 77.1 79.0 Year2016 2017 2018
MSCOCO INSTANCE SEGMENTATION CHALLENGES 2018 Overview 37.4 41.6 52.6 56.0 35 40 45 50 55 60 2015 2016 2017(Megvii) Ours Detector mmAP 28.4 37.6 46.7 48.8 25 30 35 40 45 50 55 2015 2016 2017 Ours Mask mmAP Object Detector COCO CHALLENGE 2018 PANOPTIC SEGMENTATION TASK COCO Challenge 2018 Panoptic Segmentation Task Team name: PKU_360 Team members: Yibo Yang, Xia Li, Hongyang Li, Tiancheng Shen, Zhouchen Lin, Jian Dong, Jiashi Feng, Shuicheng Yan TEAM OKS KEYPOINTS DETECTION Team OKS Keypoints Detection Yujie Wang 1*, Changbao Wang , Quanquan Li2*, Biao Leng 1, Zhoujun Li , Junjie Yan2 1Beihang University 2Sensetime Group Limited (*Equal contribution. This work was done when Yujie Wang and Changbao Wang were interns at Sensetime Group Limited) PRESENTATIONS.COCODATASET.ORG presentations.cocodataset.org 1000 false COCO17-Detect-MSRA.pdf 2020-04-01T21:24:36.000Z "2275ddc26162e380c31cf16ce39f0045" 1956170 COCO CHALLENGE 2018 PANOPTIC SEGMENTATION TASK COCO Challenge 2018 Panoptic Segmentation Task Team name: PKU_360 Team members: Yibo Yang, Xia Li, Hongyang Li, Tiancheng Shen, Zhouchen Lin, Jian Dong, Jiashi Feng, Shuicheng Yan JOINT COCO AND MAPILLARY RECOGNITION CHALLENGE WORKSHOP 1 Joint COCO and Mapillary Recognition Challenge Workshop Sunday, September 9th, ECCV 2018 Alexander Kirillov, Facebook AI Research 2018Panoptic Challenge
A UNIFIED ARCHITECTURE FOR INSTANCE AND SEMANTIC SEGMENTATION FPN Architecture 1 4 1 8 1 16 1 32 image 1 2x up 1x1 conv + high resolution low resolution strong features strong features He, K., Zhang, X., Ren, S., & Sun, J. MATTEO RUGGERO RONCHI Matteo Ruggero Ronchi COCO and Places Visual Recognition Challenges Workshop Sunday, October 29th, Venice, Italy 2017 Keypoints Challenge DEFORMABLE CONVOLUTIONAL NETWORKS XCeption -> Aligned XCeption 14x14x728 feature maps separable conv 728, 3x3, pad 1 separable conv 1024, 3x3, pad 1 max pool 3x3, stride2,pad 1 conv 1024 1x1
PANOPTIC SEGMENTATION: UNIFYING SEMANTIC AND INSTANCE Unifying Semantic and Instance Segmentation Semantic Segmentation Object Detection/Seg • per-pixel annotation • simple accuracy measure • instances indistinguishable LEARNING EFFICIENT DENSEPOSENETWORK 1 Learning Efficient DensePoseNetwork--ECCV 2018 Spotlight YuchenMa, XinzeChen, Guan Huang HorizonRobotics MAPILLARY TASKS JOINT COCO AND MAPILLARY RECOGNITION CHALLENGE Mapillary is a platform for extracting map data from street-level imagery using computer vision 350+ million images, 5.5 million km, 30+billion objects
TEAM MSRA KEYPOINTS DETECTION Team MSRA Keypoints Detection Bin Xiao 1, Dianqi Li 2, Ke Sun , Lei Zhang , Jingdong Wang1 1Microsoft Research Asia 2Microsoft COCO - COMMON OBJECTS IN CONTEXT info@cocodataset.org. Home; PeopleCOCO + LVIS
COCO + MAPILLARY
JOINT COCO AND MAPILLARY WORKSHOP AT ICCV 2019: COCO Joint COCO and Mapillary Workshop at ICCV 2019: COCO Keypoint Challenge Track Technical Report: Res-Steps-Net for Multi-Person Pose Estimation Yuanhao Cai 1;2 Zhicheng Wang Binyi Yin1;3 Ruihao Yin 3 Angang Du 4 Zhengxiong Luo 1;5 Zeming Li Xinyu Zhou 1Gang Yu Erjin Zhou Xiangyu Zhang 1Yichen Wei Jian Sun1 1Megvii Inc. 2Tsinghua University 3Beihang University 4Ocean University of China 2018 KEYPOINTS CHALLENGE / 24 Multiple Perspectives, Instances, Sizes, Occlusions: 3 COCO Keypoints Dataset (I) • 17 types of keypoints. • 58,945 images. • 156,165 annotated people. FPN-BASEDNETWORKFOR PANOPTIC SEGMENTATION FPN-basedNetworkfor Panoptic Segmentation CaribbeanTeam Yanwei Li* 1,2, NaiyuGao*1,2, ChaoxuGuo1,2, XinzeChen1, QianZhang1, Guan Huang1, Xin Zhao2, KaiqiHuang2 MSCOCO KEYPOINTS CHALLENGE 2018 Results on COCO 2018 test_challenge test_dev mini_validation Ensemble model 76.4(final submission) 78.1 80.0 Single model - 77.1 79.0 Year2016 2017 2018
MSCOCO INSTANCE SEGMENTATION CHALLENGES 2018 Overview 37.4 41.6 52.6 56.0 35 40 45 50 55 60 2015 2016 2017(Megvii) Ours Detector mmAP 28.4 37.6 46.7 48.8 25 30 35 40 45 50 55 2015 2016 2017 Ours Mask mmAP Object Detector COCO CHALLENGE 2018 PANOPTIC SEGMENTATION TASK COCO Challenge 2018 Panoptic Segmentation Task Team name: PKU_360 Team members: Yibo Yang, Xia Li, Hongyang Li, Tiancheng Shen, Zhouchen Lin, Jian Dong, Jiashi Feng, Shuicheng Yan TEAM OKS KEYPOINTS DETECTION Team OKS Keypoints Detection Yujie Wang 1*, Changbao Wang , Quanquan Li2*, Biao Leng 1, Zhoujun Li , Junjie Yan2 1Beihang University 2Sensetime Group Limited (*Equal contribution. This work was done when Yujie Wang and Changbao Wang were interns at Sensetime Group Limited) COCO - COMMON OBJECTS IN CONTEXT info@cocodataset.org. Home; PeopleCOCO + LVIS
COCO + MAPILLARY
JOINT COCO AND MAPILLARY WORKSHOP AT ICCV 2019: COCO Joint COCO and Mapillary Workshop at ICCV 2019: COCO Keypoint Challenge Track Technical Report: Res-Steps-Net for Multi-Person Pose Estimation Yuanhao Cai 1;2 Zhicheng Wang Binyi Yin1;3 Ruihao Yin 3 Angang Du 4 Zhengxiong Luo 1;5 Zeming Li Xinyu Zhou 1Gang Yu Erjin Zhou Xiangyu Zhang 1Yichen Wei Jian Sun1 1Megvii Inc. 2Tsinghua University 3Beihang University 4Ocean University of China 2018 KEYPOINTS CHALLENGE / 24 Multiple Perspectives, Instances, Sizes, Occlusions: 3 COCO Keypoints Dataset (I) • 17 types of keypoints. • 58,945 images. • 156,165 annotated people. FPN-BASEDNETWORKFOR PANOPTIC SEGMENTATION FPN-basedNetworkfor Panoptic Segmentation CaribbeanTeam Yanwei Li* 1,2, NaiyuGao*1,2, ChaoxuGuo1,2, XinzeChen1, QianZhang1, Guan Huang1, Xin Zhao2, KaiqiHuang2 MSCOCO KEYPOINTS CHALLENGE 2018 Results on COCO 2018 test_challenge test_dev mini_validation Ensemble model 76.4(final submission) 78.1 80.0 Single model - 77.1 79.0 Year2016 2017 2018
MSCOCO INSTANCE SEGMENTATION CHALLENGES 2018 Overview 37.4 41.6 52.6 56.0 35 40 45 50 55 60 2015 2016 2017(Megvii) Ours Detector mmAP 28.4 37.6 46.7 48.8 25 30 35 40 45 50 55 2015 2016 2017 Ours Mask mmAP Object Detector COCO CHALLENGE 2018 PANOPTIC SEGMENTATION TASK COCO Challenge 2018 Panoptic Segmentation Task Team name: PKU_360 Team members: Yibo Yang, Xia Li, Hongyang Li, Tiancheng Shen, Zhouchen Lin, Jian Dong, Jiashi Feng, Shuicheng Yan TEAM OKS KEYPOINTS DETECTION Team OKS Keypoints Detection Yujie Wang 1*, Changbao Wang , Quanquan Li2*, Biao Leng 1, Zhoujun Li , Junjie Yan2 1Beihang University 2Sensetime Group Limited (*Equal contribution. This work was done when Yujie Wang and Changbao Wang were interns at Sensetime Group Limited) PRESENTATIONS.COCODATASET.ORG presentations.cocodataset.org 1000 false COCO17-Detect-MSRA.pdf 2020-04-01T21:24:36.000Z "2275ddc26162e380c31cf16ce39f0045" 1956170 COCO CHALLENGE 2018 PANOPTIC SEGMENTATION TASK COCO Challenge 2018 Panoptic Segmentation Task Team name: PKU_360 Team members: Yibo Yang, Xia Li, Hongyang Li, Tiancheng Shen, Zhouchen Lin, Jian Dong, Jiashi Feng, Shuicheng Yan JOINT COCO AND MAPILLARY RECOGNITION CHALLENGE WORKSHOP 1 Joint COCO and Mapillary Recognition Challenge Workshop Sunday, September 9th, ECCV 2018 Alexander Kirillov, Facebook AI Research 2018Panoptic Challenge
A UNIFIED ARCHITECTURE FOR INSTANCE AND SEMANTIC SEGMENTATION FPN Architecture 1 4 1 8 1 16 1 32 image 1 2x up 1x1 conv + high resolution low resolution strong features strong features He, K., Zhang, X., Ren, S., & Sun, J. MATTEO RUGGERO RONCHI Matteo Ruggero Ronchi COCO and Places Visual Recognition Challenges Workshop Sunday, October 29th, Venice, Italy 2017 Keypoints Challenge DEFORMABLE CONVOLUTIONAL NETWORKS XCeption -> Aligned XCeption 14x14x728 feature maps separable conv 728, 3x3, pad 1 separable conv 1024, 3x3, pad 1 max pool 3x3, stride2,pad 1 conv 1024 1x1
PANOPTIC SEGMENTATION: UNIFYING SEMANTIC AND INSTANCE Unifying Semantic and Instance Segmentation Semantic Segmentation Object Detection/Seg • per-pixel annotation • simple accuracy measure • instances indistinguishable LEARNING EFFICIENT DENSEPOSENETWORK 1 Learning Efficient DensePoseNetwork--ECCV 2018 Spotlight YuchenMa, XinzeChen, Guan Huang HorizonRobotics MAPILLARY TASKS JOINT COCO AND MAPILLARY RECOGNITION CHALLENGE Mapillary is a platform for extracting map data from street-level imagery using computer vision 350+ million images, 5.5 million km, 30+billion objects
TEAM MSRA KEYPOINTS DETECTION Team MSRA Keypoints Detection Bin Xiao 1, Dianqi Li 2, Ke Sun , Lei Zhang , Jingdong Wang1 1Microsoft Research Asia 2Microsoft COCO - COMMON OBJECTS IN CONTEXT info@cocodataset.org. Home; PeopleCOCO + LVIS
COCO + MAPILLARY
JOINT COCO AND MAPILLARY WORKSHOP AT ICCV 2019: COCO Joint COCO and Mapillary Workshop at ICCV 2019: COCO Keypoint Challenge Track Technical Report: Res-Steps-Net for Multi-Person Pose Estimation Yuanhao Cai 1;2 Zhicheng Wang Binyi Yin1;3 Ruihao Yin 3 Angang Du 4 Zhengxiong Luo 1;5 Zeming Li Xinyu Zhou 1Gang Yu Erjin Zhou Xiangyu Zhang 1Yichen Wei Jian Sun1 1Megvii Inc. 2Tsinghua University 3Beihang University 4Ocean University of China 2018 KEYPOINTS CHALLENGE / 24 Multiple Perspectives, Instances, Sizes, Occlusions: 3 COCO Keypoints Dataset (I) • 17 types of keypoints. • 58,945 images. • 156,165 annotated people. FPN-BASEDNETWORKFOR PANOPTIC SEGMENTATION FPN-basedNetworkfor Panoptic Segmentation CaribbeanTeam Yanwei Li* 1,2, NaiyuGao*1,2, ChaoxuGuo1,2, XinzeChen1, QianZhang1, Guan Huang1, Xin Zhao2, KaiqiHuang2 MSCOCO KEYPOINTS CHALLENGE 2018 Results on COCO 2018 test_challenge test_dev mini_validation Ensemble model 76.4(final submission) 78.1 80.0 Single model - 77.1 79.0 Year2016 2017 2018
MSCOCO INSTANCE SEGMENTATION CHALLENGES 2018 Overview 37.4 41.6 52.6 56.0 35 40 45 50 55 60 2015 2016 2017(Megvii) Ours Detector mmAP 28.4 37.6 46.7 48.8 25 30 35 40 45 50 55 2015 2016 2017 Ours Mask mmAP Object Detector COCO CHALLENGE 2018 PANOPTIC SEGMENTATION TASK COCO Challenge 2018 Panoptic Segmentation Task Team name: PKU_360 Team members: Yibo Yang, Xia Li, Hongyang Li, Tiancheng Shen, Zhouchen Lin, Jian Dong, Jiashi Feng, Shuicheng Yan TEAM OKS KEYPOINTS DETECTION Team OKS Keypoints Detection Yujie Wang 1*, Changbao Wang , Quanquan Li2*, Biao Leng 1, Zhoujun Li , Junjie Yan2 1Beihang University 2Sensetime Group Limited (*Equal contribution. This work was done when Yujie Wang and Changbao Wang were interns at Sensetime Group Limited) COCO - COMMON OBJECTS IN CONTEXT info@cocodataset.org. Home; PeopleCOCO + LVIS
COCO + MAPILLARY
JOINT COCO AND MAPILLARY WORKSHOP AT ICCV 2019: COCO Joint COCO and Mapillary Workshop at ICCV 2019: COCO Keypoint Challenge Track Technical Report: Res-Steps-Net for Multi-Person Pose Estimation Yuanhao Cai 1;2 Zhicheng Wang Binyi Yin1;3 Ruihao Yin 3 Angang Du 4 Zhengxiong Luo 1;5 Zeming Li Xinyu Zhou 1Gang Yu Erjin Zhou Xiangyu Zhang 1Yichen Wei Jian Sun1 1Megvii Inc. 2Tsinghua University 3Beihang University 4Ocean University of China 2018 KEYPOINTS CHALLENGE / 24 Multiple Perspectives, Instances, Sizes, Occlusions: 3 COCO Keypoints Dataset (I) • 17 types of keypoints. • 58,945 images. • 156,165 annotated people. FPN-BASEDNETWORKFOR PANOPTIC SEGMENTATION FPN-basedNetworkfor Panoptic Segmentation CaribbeanTeam Yanwei Li* 1,2, NaiyuGao*1,2, ChaoxuGuo1,2, XinzeChen1, QianZhang1, Guan Huang1, Xin Zhao2, KaiqiHuang2 MSCOCO KEYPOINTS CHALLENGE 2018 Results on COCO 2018 test_challenge test_dev mini_validation Ensemble model 76.4(final submission) 78.1 80.0 Single model - 77.1 79.0 Year2016 2017 2018
MSCOCO INSTANCE SEGMENTATION CHALLENGES 2018 Overview 37.4 41.6 52.6 56.0 35 40 45 50 55 60 2015 2016 2017(Megvii) Ours Detector mmAP 28.4 37.6 46.7 48.8 25 30 35 40 45 50 55 2015 2016 2017 Ours Mask mmAP Object Detector COCO CHALLENGE 2018 PANOPTIC SEGMENTATION TASK COCO Challenge 2018 Panoptic Segmentation Task Team name: PKU_360 Team members: Yibo Yang, Xia Li, Hongyang Li, Tiancheng Shen, Zhouchen Lin, Jian Dong, Jiashi Feng, Shuicheng Yan TEAM OKS KEYPOINTS DETECTION Team OKS Keypoints Detection Yujie Wang 1*, Changbao Wang , Quanquan Li2*, Biao Leng 1, Zhoujun Li , Junjie Yan2 1Beihang University 2Sensetime Group Limited (*Equal contribution. This work was done when Yujie Wang and Changbao Wang were interns at Sensetime Group Limited) PRESENTATIONS.COCODATASET.ORG presentations.cocodataset.org 1000 false COCO17-Detect-MSRA.pdf 2020-04-01T21:24:36.000Z "2275ddc26162e380c31cf16ce39f0045" 1956170 COCO CHALLENGE 2018 PANOPTIC SEGMENTATION TASK COCO Challenge 2018 Panoptic Segmentation Task Team name: PKU_360 Team members: Yibo Yang, Xia Li, Hongyang Li, Tiancheng Shen, Zhouchen Lin, Jian Dong, Jiashi Feng, Shuicheng Yan JOINT COCO AND MAPILLARY RECOGNITION CHALLENGE WORKSHOP 1 Joint COCO and Mapillary Recognition Challenge Workshop Sunday, September 9th, ECCV 2018 Alexander Kirillov, Facebook AI Research 2018Panoptic Challenge
A UNIFIED ARCHITECTURE FOR INSTANCE AND SEMANTIC SEGMENTATION FPN Architecture 1 4 1 8 1 16 1 32 image 1 2x up 1x1 conv + high resolution low resolution strong features strong features He, K., Zhang, X., Ren, S., & Sun, J. MATTEO RUGGERO RONCHI Matteo Ruggero Ronchi COCO and Places Visual Recognition Challenges Workshop Sunday, October 29th, Venice, Italy 2017 Keypoints Challenge DEFORMABLE CONVOLUTIONAL NETWORKS XCeption -> Aligned XCeption 14x14x728 feature maps separable conv 728, 3x3, pad 1 separable conv 1024, 3x3, pad 1 max pool 3x3, stride2,pad 1 conv 1024 1x1
PANOPTIC SEGMENTATION: UNIFYING SEMANTIC AND INSTANCE Unifying Semantic and Instance Segmentation Semantic Segmentation Object Detection/Seg • per-pixel annotation • simple accuracy measure • instances indistinguishable LEARNING EFFICIENT DENSEPOSENETWORK 1 Learning Efficient DensePoseNetwork--ECCV 2018 Spotlight YuchenMa, XinzeChen, Guan Huang HorizonRobotics MAPILLARY TASKS JOINT COCO AND MAPILLARY RECOGNITION CHALLENGE Mapillary is a platform for extracting map data from street-level imagery using computer vision 350+ million images, 5.5 million km, 30+billion objects
TEAM MSRA KEYPOINTS DETECTION Team MSRA Keypoints Detection Bin Xiao 1, Dianqi Li 2, Ke Sun , Lei Zhang , Jingdong Wang1 1Microsoft Research Asia 2Microsoft COCO - COMMON OBJECTS IN CONTEXT info@cocodataset.org. Home; PeopleCOCO + LVIS
JOINT COCO AND MAPILLARY WORKSHOP AT ICCV 2019: COCO Joint COCO and Mapillary Workshop at ICCV 2019: COCO Keypoint Challenge Track Technical Report: Res-Steps-Net for Multi-Person Pose Estimation Yuanhao Cai 1;2 Zhicheng Wang Binyi Yin1;3 Ruihao Yin 3 Angang Du 4 Zhengxiong Luo 1;5 Zeming Li Xinyu Zhou 1Gang Yu Erjin Zhou Xiangyu Zhang 1Yichen Wei Jian Sun1 1Megvii Inc. 2Tsinghua University 3Beihang University 4Ocean University of China JOINT COCO AND MAPILLARY RECOGNITION CHALLENGE WORKSHOP 1 Joint COCO and Mapillary Recognition Challenge Workshop Sunday, September 9th, ECCV 2018 Alexander Kirillov, Facebook AI Research 2018Panoptic Challenge
MSCOCO KEYPOINTS CHALLENGE 2018 Results on COCO 2018 test_challenge test_dev mini_validation Ensemble model 76.4(final submission) 78.1 80.0 Single model - 77.1 79.0 Year2016 2017 2018
DEFORMABLE CONVOLUTIONAL NETWORKS XCeption -> Aligned XCeption 14x14x728 feature maps separable conv 728, 3x3, pad 1 separable conv 1024, 3x3, pad 1 max pool 3x3, stride2,pad 1 conv 1024 1x1
A UNIFIED ARCHITECTURE FOR INSTANCE AND SEMANTIC SEGMENTATION FPN Architecture 1 4 1 8 1 16 1 32 image 1 2x up 1x1 conv + high resolution low resolution strong features strong features He, K., Zhang, X., Ren, S., & Sun, J. MSCOCO INSTANCE SEGMENTATION CHALLENGES 2018 Overview 37.4 41.6 52.6 56.0 35 40 45 50 55 60 2015 2016 2017(Megvii) Ours Detector mmAP 28.4 37.6 46.7 48.8 25 30 35 40 45 50 55 2015 2016 2017 Ours Mask mmAP Object Detector TEAM OKS KEYPOINTS DETECTION Team OKS Keypoints Detection Yujie Wang 1*, Changbao Wang , Quanquan Li2*, Biao Leng 1, Zhoujun Li , Junjie Yan2 1Beihang University 2Sensetime Group Limited (*Equal contribution. This work was done when Yujie Wang and Changbao Wang were interns at Sensetime Group Limited) FPN-BASEDNETWORKFOR PANOPTIC SEGMENTATION FPN-basedNetworkfor Panoptic Segmentation CaribbeanTeam Yanwei Li* 1,2, NaiyuGao*1,2, ChaoxuGuo1,2, XinzeChen1, QianZhang1, Guan Huang1, Xin Zhao2, KaiqiHuang2 COCO - COMMON OBJECTS IN CONTEXT info@cocodataset.org. Home; PeopleCOCO + LVIS
JOINT COCO AND MAPILLARY WORKSHOP AT ICCV 2019: COCO Joint COCO and Mapillary Workshop at ICCV 2019: COCO Keypoint Challenge Track Technical Report: Res-Steps-Net for Multi-Person Pose Estimation Yuanhao Cai 1;2 Zhicheng Wang Binyi Yin1;3 Ruihao Yin 3 Angang Du 4 Zhengxiong Luo 1;5 Zeming Li Xinyu Zhou 1Gang Yu Erjin Zhou Xiangyu Zhang 1Yichen Wei Jian Sun1 1Megvii Inc. 2Tsinghua University 3Beihang University 4Ocean University of China JOINT COCO AND MAPILLARY RECOGNITION CHALLENGE WORKSHOP 1 Joint COCO and Mapillary Recognition Challenge Workshop Sunday, September 9th, ECCV 2018 Alexander Kirillov, Facebook AI Research 2018Panoptic Challenge
MSCOCO KEYPOINTS CHALLENGE 2018 Results on COCO 2018 test_challenge test_dev mini_validation Ensemble model 76.4(final submission) 78.1 80.0 Single model - 77.1 79.0 Year2016 2017 2018
DEFORMABLE CONVOLUTIONAL NETWORKS XCeption -> Aligned XCeption 14x14x728 feature maps separable conv 728, 3x3, pad 1 separable conv 1024, 3x3, pad 1 max pool 3x3, stride2,pad 1 conv 1024 1x1
A UNIFIED ARCHITECTURE FOR INSTANCE AND SEMANTIC SEGMENTATION FPN Architecture 1 4 1 8 1 16 1 32 image 1 2x up 1x1 conv + high resolution low resolution strong features strong features He, K., Zhang, X., Ren, S., & Sun, J. MSCOCO INSTANCE SEGMENTATION CHALLENGES 2018 Overview 37.4 41.6 52.6 56.0 35 40 45 50 55 60 2015 2016 2017(Megvii) Ours Detector mmAP 28.4 37.6 46.7 48.8 25 30 35 40 45 50 55 2015 2016 2017 Ours Mask mmAP Object Detector TEAM OKS KEYPOINTS DETECTION Team OKS Keypoints Detection Yujie Wang 1*, Changbao Wang , Quanquan Li2*, Biao Leng 1, Zhoujun Li , Junjie Yan2 1Beihang University 2Sensetime Group Limited (*Equal contribution. This work was done when Yujie Wang and Changbao Wang were interns at Sensetime Group Limited) FPN-BASEDNETWORKFOR PANOPTIC SEGMENTATION FPN-basedNetworkfor Panoptic Segmentation CaribbeanTeam Yanwei Li* 1,2, NaiyuGao*1,2, ChaoxuGuo1,2, XinzeChen1, QianZhang1, Guan Huang1, Xin Zhao2, KaiqiHuang2 PRESENTATIONS.COCODATASET.ORG presentations.cocodataset.org 1000 false COCO17-Detect-MSRA.pdf 2020-04-01T21:24:36.000Z "2275ddc26162e380c31cf16ce39f0045" 1956170 COCO + MAPILLARY 2018 3. COCO Challenges. COCO is an image dataset designed to spur object detection research with a focus on detecting objects in context. The annotations include instance segmentations for object belonging to 80 categories, stuff segmentations for 91 categories, keypoint annotations for person instances, and five image captions per image.COCODATASET.ORG
cocodataset.org
JOINT COCO AND MAPILLARY WORKSHOP AT ICCV 2019: PANOPTIC PAM MCB Huge Backbone mIOU 49.3 X 49.9 X X 50.4 X X X 53.9 Table 1: Ablation study on COCO validation set. PAM rep-resents Parallel Attention Module, MCB represents Multi- A UNIFIED ARCHITECTURE FOR INSTANCE AND SEMANTIC SEGMENTATION FPN Architecture 1 4 1 8 1 16 1 32 image 1 2x up 1x1 conv + high resolution low resolution strong features strong features He, K., Zhang, X., Ren, S., & Sun, J. MATTEO RUGGERO RONCHI Matteo Ruggero Ronchi COCO and Places Visual Recognition Challenges Workshop Sunday, October 29th, Venice, Italy 2017 Keypoints Challenge FPN-BASEDNETWORKFOR PANOPTIC SEGMENTATION FPN-basedNetworkfor Panoptic Segmentation CaribbeanTeam Yanwei Li* 1,2, NaiyuGao*1,2, ChaoxuGuo1,2, XinzeChen1, QianZhang1, Guan Huang1, Xin Zhao2, KaiqiHuang2 LEARNING EFFICIENT DENSEPOSENETWORK 1 Learning Efficient DensePoseNetwork--ECCV 2018 Spotlight YuchenMa, XinzeChen, Guan Huang HorizonRobotics PANOPTIC SEGMENTATION: UNIFYING SEMANTIC AND INSTANCE Unifying Semantic and Instance Segmentation Semantic Segmentation Object Detection/Seg • per-pixel annotation • simple accuracy measure • instances indistinguishable PLACES CHALLENGE 2017 3 Basic information of the data • 20210 images for training, and 2000 images for validation, 3352 images for testing • 150 labels including 35 stuff concepts and 115 discrete objects COCO - COMMON OBJECTS IN CONTEXT info@cocodataset.org. Home; PeopleCOCO + LVIS
JOINT COCO AND MAPILLARY WORKSHOP AT ICCV 2019: COCO Joint COCO and Mapillary Workshop at ICCV 2019: COCO Keypoint Challenge Track Technical Report: Res-Steps-Net for Multi-Person Pose Estimation Yuanhao Cai 1;2 Zhicheng Wang Binyi Yin1;3 Ruihao Yin 3 Angang Du 4 Zhengxiong Luo 1;5 Zeming Li Xinyu Zhou 1Gang Yu Erjin Zhou Xiangyu Zhang 1Yichen Wei Jian Sun1 1Megvii Inc. 2Tsinghua University 3Beihang University 4Ocean University of China JOINT COCO AND MAPILLARY RECOGNITION CHALLENGE WORKSHOPCOCO CHALLENGE 2020COCO DATASET 2017COCO DATASETSCOCO IMAGE DATASETDOWNLOAD COCO DATASETCOCO KEY POINTS 1 Joint COCO and Mapillary Recognition Challenge Workshop Sunday, September 9th, ECCV 2018 Alexander Kirillov, Facebook AI Research 2018Panoptic Challenge
MSCOCO KEYPOINTS CHALLENGE 2018 Results on COCO 2018 test_challenge test_dev mini_validation Ensemble model 76.4(final submission) 78.1 80.0 Single model - 77.1 79.0 Year2016 2017 2018
DEFORMABLE CONVOLUTIONAL NETWORKS XCeption -> Aligned XCeption 14x14x728 feature maps separable conv 728, 3x3, pad 1 separable conv 1024, 3x3, pad 1 max pool 3x3, stride2,pad 1 conv 1024 1x1
A UNIFIED ARCHITECTURE FOR INSTANCE AND SEMANTIC SEGMENTATION FPN Architecture 1 4 1 8 1 16 1 32 image 1 2x up 1x1 conv + high resolution low resolution strong features strong features He, K., Zhang, X., Ren, S., & Sun, J. MSCOCO INSTANCE SEGMENTATION CHALLENGES 2018 Overview 37.4 41.6 52.6 56.0 35 40 45 50 55 60 2015 2016 2017(Megvii) Ours Detector mmAP 28.4 37.6 46.7 48.8 25 30 35 40 45 50 55 2015 2016 2017 Ours Mask mmAP Object Detector TEAM OKS KEYPOINTS DETECTION Team OKS Keypoints Detection Yujie Wang 1*, Changbao Wang , Quanquan Li2*, Biao Leng 1, Zhoujun Li , Junjie Yan2 1Beihang University 2Sensetime Group Limited (*Equal contribution. This work was done when Yujie Wang and Changbao Wang were interns at Sensetime Group Limited) FPN-BASEDNETWORKFOR PANOPTIC SEGMENTATION FPN-basedNetworkfor Panoptic Segmentation CaribbeanTeam Yanwei Li* 1,2, NaiyuGao*1,2, ChaoxuGuo1,2, XinzeChen1, QianZhang1, Guan Huang1, Xin Zhao2, KaiqiHuang2 COCO - COMMON OBJECTS IN CONTEXT info@cocodataset.org. Home; PeopleCOCO + LVIS
JOINT COCO AND MAPILLARY WORKSHOP AT ICCV 2019: COCO Joint COCO and Mapillary Workshop at ICCV 2019: COCO Keypoint Challenge Track Technical Report: Res-Steps-Net for Multi-Person Pose Estimation Yuanhao Cai 1;2 Zhicheng Wang Binyi Yin1;3 Ruihao Yin 3 Angang Du 4 Zhengxiong Luo 1;5 Zeming Li Xinyu Zhou 1Gang Yu Erjin Zhou Xiangyu Zhang 1Yichen Wei Jian Sun1 1Megvii Inc. 2Tsinghua University 3Beihang University 4Ocean University of China JOINT COCO AND MAPILLARY RECOGNITION CHALLENGE WORKSHOPCOCO CHALLENGE 2020COCO DATASET 2017COCO DATASETSCOCO IMAGE DATASETDOWNLOAD COCO DATASETCOCO KEY POINTS 1 Joint COCO and Mapillary Recognition Challenge Workshop Sunday, September 9th, ECCV 2018 Alexander Kirillov, Facebook AI Research 2018Panoptic Challenge
MSCOCO KEYPOINTS CHALLENGE 2018 Results on COCO 2018 test_challenge test_dev mini_validation Ensemble model 76.4(final submission) 78.1 80.0 Single model - 77.1 79.0 Year2016 2017 2018
DEFORMABLE CONVOLUTIONAL NETWORKS XCeption -> Aligned XCeption 14x14x728 feature maps separable conv 728, 3x3, pad 1 separable conv 1024, 3x3, pad 1 max pool 3x3, stride2,pad 1 conv 1024 1x1
A UNIFIED ARCHITECTURE FOR INSTANCE AND SEMANTIC SEGMENTATION FPN Architecture 1 4 1 8 1 16 1 32 image 1 2x up 1x1 conv + high resolution low resolution strong features strong features He, K., Zhang, X., Ren, S., & Sun, J. MSCOCO INSTANCE SEGMENTATION CHALLENGES 2018 Overview 37.4 41.6 52.6 56.0 35 40 45 50 55 60 2015 2016 2017(Megvii) Ours Detector mmAP 28.4 37.6 46.7 48.8 25 30 35 40 45 50 55 2015 2016 2017 Ours Mask mmAP Object Detector TEAM OKS KEYPOINTS DETECTION Team OKS Keypoints Detection Yujie Wang 1*, Changbao Wang , Quanquan Li2*, Biao Leng 1, Zhoujun Li , Junjie Yan2 1Beihang University 2Sensetime Group Limited (*Equal contribution. This work was done when Yujie Wang and Changbao Wang were interns at Sensetime Group Limited) FPN-BASEDNETWORKFOR PANOPTIC SEGMENTATION FPN-basedNetworkfor Panoptic Segmentation CaribbeanTeam Yanwei Li* 1,2, NaiyuGao*1,2, ChaoxuGuo1,2, XinzeChen1, QianZhang1, Guan Huang1, Xin Zhao2, KaiqiHuang2 PRESENTATIONS.COCODATASET.ORG presentations.cocodataset.org 1000 false COCO17-Detect-MSRA.pdf 2020-04-01T21:24:36.000Z "2275ddc26162e380c31cf16ce39f0045" 1956170 COCO + MAPILLARY 2018 3. COCO Challenges. COCO is an image dataset designed to spur object detection research with a focus on detecting objects in context. The annotations include instance segmentations for object belonging to 80 categories, stuff segmentations for 91 categories, keypoint annotations for person instances, and five image captions per image.COCODATASET.ORG
cocodataset.org
JOINT COCO AND MAPILLARY WORKSHOP AT ICCV 2019: PANOPTIC PAM MCB Huge Backbone mIOU 49.3 X 49.9 X X 50.4 X X X 53.9 Table 1: Ablation study on COCO validation set. PAM rep-resents Parallel Attention Module, MCB represents Multi- A UNIFIED ARCHITECTURE FOR INSTANCE AND SEMANTIC SEGMENTATION FPN Architecture 1 4 1 8 1 16 1 32 image 1 2x up 1x1 conv + high resolution low resolution strong features strong features He, K., Zhang, X., Ren, S., & Sun, J. MATTEO RUGGERO RONCHI Matteo Ruggero Ronchi COCO and Places Visual Recognition Challenges Workshop Sunday, October 29th, Venice, Italy 2017 Keypoints Challenge FPN-BASEDNETWORKFOR PANOPTIC SEGMENTATION FPN-basedNetworkfor Panoptic Segmentation CaribbeanTeam Yanwei Li* 1,2, NaiyuGao*1,2, ChaoxuGuo1,2, XinzeChen1, QianZhang1, Guan Huang1, Xin Zhao2, KaiqiHuang2 LEARNING EFFICIENT DENSEPOSENETWORK 1 Learning Efficient DensePoseNetwork--ECCV 2018 Spotlight YuchenMa, XinzeChen, Guan Huang HorizonRobotics PANOPTIC SEGMENTATION: UNIFYING SEMANTIC AND INSTANCE Unifying Semantic and Instance Segmentation Semantic Segmentation Object Detection/Seg • per-pixel annotation • simple accuracy measure • instances indistinguishable PLACES CHALLENGE 2017 3 Basic information of the data • 20210 images for training, and 2000 images for validation, 3352 images for testing • 150 labels including 35 stuff concepts and 115 discrete objects info@cocodataset.org*
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NEWS
* We are pleased to announce the COCO 2020 Detection, Keypoint, Panoptic, and DensePose Challenges. * The new rules and awards for this year challenges encourageinnovative methods.
* Results to be announced at the Joint COCO and LVIS RecognitionECCV workshop.
WHAT IS COCO?
COCO is a large-scale object detection, segmentation, and captioning dataset. COCO has several features: * Object segmentation * Recognition in context * Superpixel stuff segmentation * 330K images (>200K labeled) * 1.5 million object instances * 80 object categories * 91 stuff categories * 5 captions per image * 250,000 people with keypointsCOLLABORATORS
Tsung-Yi Lin Google Brain Genevieve Patterson MSR, Trash TV Matteo R. Ronchi CaltechYin Cui Google
Michael Maire TTI-Chicago Serge Belongie Cornell Tech Lubomir Bourdev WaveOne, Inc.Ross Girshick FAIR
James Hays Georgia Tech Pietro Perona CaltechDeva Ramanan CMU
Larry Zitnick FAIR
Piotr Dollár FAIR
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