PPT-Salient Object Detection for Searched Web Images via Global

Peng Wang 1 Jingdong Wang 2 Gang Zeng 1 Jie Feng 1 Hongbin Zha 1 Shipeng Li 2 1 Key Laboratory on Machine Perception Peking University 2 Microsoft Research Asia Outline. The resulting saliency map in evitably loses information in the original image and 64257nding salient objects in it is dif64257cult We propose to detect salient objects by directly measuring the saliency of an image win dow in the original image and Scene Analysis and . Applications. 报告人：程明明. 南开大学、计算机与控制工程学. 院. http://mmcheng.net/. Contents. Global . contrast based salient region . detection. ,. PAMI 2014. Cheng. 1. Ziming Zhang. 2. Wen-Yan Lin. 3. . Philip H. S. . Torr. 1. 1. Oxford University, . 2. Boston University . 3. Brookes Vision Group. Training a generic objectness measure to produce a small set of candidate object windows, has been shown to speed up the classical sliding window object detection paradigm. We observe that generic objects with well-defined closed boundary can be discriminated by looking at the norm of gradients. Based on this observation, we propose to use a binarized normed gradients (BING) for efficient objectness estimation. Experiments on the . Oscar . Danielsson. (osda02@kth.se). Stefan . Carlsson. (. stefanc@kth.se. ). Outline. Detect all Instances of an Object Class. The classifier needs to be fast (on average). This is typically accomplished by:. Compositional bias of salient object detection benchmarking. Xiaodi. . Hou. K-Lab, Computation and Neural Systems. California Institute of Technology. for the Crash Course on Visual Saliency Modeling:. Facebook AI Research. Wenchi. Ma. Data: 11/04/2016. More information from object detection. More information from object detection. More information from object detection. Object Detection for now with Deep Learning. Authors:. Farnaz Shariat , . Riadh Ksantini, . Boubakeur . Boufama. shariatf@uwindsor.ca. ksantini@uwindsor.ca. boufama@uwindsor.ca. University of Windsor. May 2009. 2. Presentation Outline . Introduction . 3D Geometric Reasoning. Jiyan. Pan. 12/3/2012. Task. Detect objects. Identify surface regions. Estimate ground plane. Infer gravity direction. Geometrically coherent in the. 3D world. 3D geometric context. Jie Feng. 1. , Yichen Wei. 2. , Litian Tao. 3. , Chao Zhang. 1. , Jian Sun. 2. 1. Key Laboratory of Machine Perception, Peking University. 2. Microsoft . Research . Asia. 3. Microsoft Search Technology Center Asia. AdaScale: Towards Real-time Video Object Detection using Adaptive Scaling Ting-Wu (Rudy) Chin* Ruizhuo Ding* Diana Marculescu ECE Dept., Carnegie Mellon University SysML 2019 Autonomous Cars Li et al, 2018 . Outline. Background. Methods. Results. Background. Object . detection. : . classification. + . . localization. Classifcation. : . what. . is. the . object. ?. Localization. : . where. of . Deformable Animals in Images. Advisers:. Prof. C.V. . Jawahar. Prof. A. . P.Zisserman. 3. rd. August 2011. Omkar. M. . Parkhi. 200807012. Object Category Recognition. Popular in the community since long time.. Applications. 报告人：程明明. 南开大学、计算机与控制工程学. 院. http://mmcheng.net/. Contents. Global . contrast based salient region . detection. ,. PAMI 2014. BING: Binarized Normed Gradients for Objectness Estimation at . Xindian. Long. 2018.09. Outline. Introduction. Object Detection Concept and the YOLO Algorithm. Object Detection Example (CAS Action). Facial Keypoint Detection Example (. DLPy. ). Why SAS Deep Learning .