Object detection

Results: 550



#Item
21Feature Pyramid Networks for Object Detection Tsung-Yi Lin1,2 , Piotr Doll´ar1 , Ross Girshick1 , Kaiming He1 , Bharath Hariharan1 , and Serge Belongie2 1 arXiv:1612.03144v1 [cs.CV] 9 Dec 2016

Feature Pyramid Networks for Object Detection Tsung-Yi Lin1,2 , Piotr Doll´ar1 , Ross Girshick1 , Kaiming He1 , Bharath Hariharan1 , and Serge Belongie2 1 arXiv:1612.03144v1 [cs.CV] 9 Dec 2016

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Source URL: vision.cornell.edu

- Date: 2016-12-11 21:28:27
    22Describing the Scene as a Whole: Joint Object Detection, Scene Classification and Semantic Segmentation Jian Yao TTI Chicago Sanja Fidler

    Describing the Scene as a Whole: Joint Object Detection, Scene Classification and Semantic Segmentation Jian Yao TTI Chicago Sanja Fidler

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    Source URL: ttic.uchicago.edu

    - Date: 2012-07-10 14:29:21
      23DOTA: A Large-scale Dataset for Object Detection in Aerial Images∗ Gui-Song Xia1†, Xiang Bai2†, Jian Ding1 , Zhen Zhu2 , Serge Belongie3 , Jiebo Luo4 , Mihai Datcu5 , Marcello Pelillo6 , Liangpei Zhang1 1 Wuhan Uni

      DOTA: A Large-scale Dataset for Object Detection in Aerial Images∗ Gui-Song Xia1†, Xiang Bai2†, Jian Ding1 , Zhen Zhu2 , Serge Belongie3 , Jiebo Luo4 , Mihai Datcu5 , Marcello Pelillo6 , Liangpei Zhang1 1 Wuhan Uni

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      Source URL: vision.cornell.edu

      - Date: 2018-03-28 14:49:29
        24Joint Pose Estimator and Feature Learning for Object Detection Karim Ali1,2 , Franc¸ois Fleuret1,3 , David Hasler2 , Pascal Fua1 1 ´ Ecole

        Joint Pose Estimator and Feature Learning for Object Detection Karim Ali1,2 , Franc¸ois Fleuret1,3 , David Hasler2 , Pascal Fua1 1 ´ Ecole

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        Source URL: www.karimali.org

        - Date: 2009-07-16 07:23:39
          25Object Detection with Grammar Models Ross B. Girshick Dept. of Computer Science University of Chicago Chicago, IL 60637

          Object Detection with Grammar Models Ross B. Girshick Dept. of Computer Science University of Chicago Chicago, IL 60637

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          Source URL: people.cs.uchicago.edu

          - Date: 2012-09-05 14:50:32
            26Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks Shaoqing Ren∗ Kaiming He Ross Girshick

            Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks Shaoqing Ren∗ Kaiming He Ross Girshick

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            Source URL: papers.nips.cc

            - Date: 2016-02-16 15:47:49
              27IEEE International Conference on Image Processing (ICIP), Melbourne, Australia, COMBINING CONTOUR AND SHAPE PRIMITIVES FOR OBJECT DETECTION AND POSE ESTIMATION OF PREFABRICATED PARTS Alexander Berner, Jun Li, Dirk

              IEEE International Conference on Image Processing (ICIP), Melbourne, Australia, COMBINING CONTOUR AND SHAPE PRIMITIVES FOR OBJECT DETECTION AND POSE ESTIMATION OF PREFABRICATED PARTS Alexander Berner, Jun Li, Dirk

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              Source URL: ais.uni-bonn.de

              - Date: 2013-06-07 07:46:15
                28HARF: Hierarchy-associated Rich Features for Salient Object Detection Wenbin Zou Shenzhen Key Lab of Advanced Telecommunication and Information Processing College of Information Engineering, Shenzhen University zouszu@si

                HARF: Hierarchy-associated Rich Features for Salient Object Detection Wenbin Zou Shenzhen Key Lab of Advanced Telecommunication and Information Processing College of Information Engineering, Shenzhen University zouszu@si

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                Source URL: www.cv-foundation.org

                - Date: 2015-10-24 15:00:28
                  29A Easy, Fast and Energy Efficient Object Detection on Heterogeneous On-Chip Architectures1 Ehsan Totoni, University of Illinois at Urbana-Champaign Mert Dikmen, University of Illinois at Urbana-Champaign ´ , University

                  A Easy, Fast and Energy Efficient Object Detection on Heterogeneous On-Chip Architectures1 Ehsan Totoni, University of Illinois at Urbana-Champaign Mert Dikmen, University of Illinois at Urbana-Champaign ´ , University

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                  Source URL: charm.cs.illinois.edu

                    30High-for-Low and Low-for-High: Efficient Boundary Detection from Deep Object Features and its Applications to High-Level Vision Gedas Bertasius University of Pennsylvania

                    High-for-Low and Low-for-High: Efficient Boundary Detection from Deep Object Features and its Applications to High-Level Vision Gedas Bertasius University of Pennsylvania

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                    Source URL: www.cv-foundation.org

                    - Date: 2015-10-24 15:01:50