Object-Contextual Representations for Semantic Segmentation. Depu Meng, Xiaokang Chen, Zejia Fan, Gang Zeng, Houqiang Li, Yuhui Yuan, Lei Sun, and Jingdong Wang. Ĭonditional DETR for Fast Training Convergence. Qi Chen, Bing Zhao, Haidong Wang, Mingqin Li, Chuanjie Liu, Zengzhong Li, Mao Yang, and Jingdong Wang. SPANN: Highly-efficient Billion-scale ApproximateNearest Neighbor Search. Yuhui Yuan, Rao Fu, Lang Huang, Weihong Lin, Chao Zhang, Xilin Chen, and Jingdong Wang. HRFormer: High-Resolution Transformer for Dense Prediction. Learning to detect a salient object (TPAMI):
#Jing dong google scholar pdf#
Pdf (CVPR) pdf (IJCV) c++ code matlab code project | Trinary-projection trees (TPAMI, CVPR 2010):Ĭomposite quantization (TPAMI, ICML 2014):ĭiscriminative Regional Feature Integration (IJCV, CVPR 2013): Neighborhood graph construction (CVPR 2012): Large-scale indexing for similarity search. Other applications pdf (short) pdf (long) code: Three papers are accepted by CVPR 2018.Ī replacement of classification networks for computer vision problems projects. Two papers are accepted by ACM MM 2018.Ģ. Second place entry, COCO keypoints detection challenge ECCV 2018.ģ. Gave a keynote talk about approximate nearest neighbor search on at JD.com.ħ. Elected as an ACM Distinguished Member, 11/2018.Ĩ. Invited as an area chair of ICCV 2019, and IJCAI 2019.ĩ. Fast neighborhood graph-based approximate nearest neighbor search: code. A replacement of classification networks for visual recognition. Cityscapes segmentation leaderboard (July 2019).ġ2. HRNet + OCR is ranked 1 on cityscapes segmentation. Invited as an area chair of CVPR 2020, ECCV 2020, and IJCAI 2020.ġ3. The implementation of HRNet + OCR is available: codeġ4. Cityscapes segmentation leaderboard (January2020). HRNet + OCR + SegFix is ranked 1 on cityscapes segmentation. On human pose estimation, semantic segmentation, object detection, face alignment, and so on. HRNet is a stronger backbone, and acheives superior performance This is a longer version of the HRNet paper published in CVPR 2019. HRNet: Deep High-Resolution Representation Learning for Visual Recognition. Code released for our CVPR 2021 paper,īottom-Up Human Pose Estimation via Disentangled Keypoint Regression. Lite-HRNet: A Lightweight High-Resolution Network. We rephrase it as Segmentation Transformer.
#Jing dong google scholar update#
Update object-contextual representation for semantic segmentation (ECCV 2020).
Welcome to the large scale approximate nearest search challenge at NeurIPS 2021: Big ANN Benchmark. Local Transformer attention is equivalent to inhomogeneousĭynamic depth-wise convolution: Demystifying local attention. Code released for our ICCV 2021 paper,Ĭonditional DETR for Fast Training Convergence. Code released for our NeurIPS 2021 paper, Elected as Fellow of IEEE, for his contributions to visual content understanding and retrieval, 11/2021.Ģ5. Transformer does not outperform CNN: On the Connection between Local Attention and Dynamic Depth-wise Convolution.Ģ7. Group DETR v2 achieves 64.5 mAP on COCO test-dev, and establishes a new SoTA onĢ8. Group DETR v2: Strong Object Detector with Encoder-Decoder Pretraining. Understanding Self-Supervised Pretraining with Part-Aware Representation Learning.
Context Autoencoder for Self-Supervised Representation Learning. Group DETR: Fast DETR Training with Group-Wise One-to-Many Assignment. He was elected as an ACM Distinguished Member, a Fellow of IAPR, and a Fellow of IEEE,įor his contributions to visual content understanding and retrieval. He has been serving/served as an Associate Editor of IEEE TPAMI, IJCV, ACM TOMM, IEEE TMM, and IEEE TCSVT,Īnd an (senior) area chair of leading conferences in vision, multimedia, and AI, such as CVPR, ICCV, ECCV, ACM MM, IJCAI, and AAAI. Neighborhood graph search (NGS, SPTAG) for vector search. Object-contextual representations (OCRNet) for semantic segmentationĭiscriminative regional feature integration (DRFI) for saliency detection, His representative works include high-resolution network (HRNet) for generic visual recognition, His areas of interest include computer vision, deep learning, and multimedia search. Before joining Baidu, he was a Senior Principal Researcher at Microsoft Research Asia from September 2007 to August 2021. Jingdong Wang is Chief Scientist for computer vision withīaidu. Jingdong Wang Jingdong Wang (王井东), Fellow of IEEE and IAPR
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