PPT-Salient Object Detection by Composition

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. 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 . 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. 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:. Jianming. Zhang, Stan . Sclaroff. , . Zhe. . lin. , . Xiaohui. Shen, Brian Price, . Radomir. . Mech. IEEE International Conference on Computer Vision (ICCV), 2015. IEEE International Conference on Computer Vision (ICCV), 2015, Santiago, Chile. Before deep . convnets. Using deep . convnets. PASCAL VOC. Beyond sliding windows: Region proposals. Advantages:. Cuts . down on number of regions detector must . evaluate. Allows detector to use more powerful features and classifiers. Leo Zhu. CSAIL MIT . Joint work with Chen, Yuille, Freeman and Torralba . 1. Ideas behind . Recursive Composition . How to deal with image complexity. A general framework for different vision tasks. Rich representation and tractable computation. 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 . Before deep . convnets. Using deep . convnets. PASCAL VOC. Beyond sliding windows: Region proposals. Advantages:. Cuts . down on number of regions detector must . evaluate. Allows detector to use more powerful features and classifiers. Yonggang Cui. 1. , Zoe N. Gastelum. 2. , Ray Ren. 1. , Michael R. Smith. 2. , . Yuewei. Lin. 1. , Maikael A. Thomas. 2. , . Shinjae. Yoo. 1. , Warren Stern. 1. 1 . Brookhaven National Laboratory, Upton, USA. 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 .