PPT-High Performance Object Detection by Collaborative Learning

of Granules Features Chang Huang and Ram Nevatia University of Southern California Institute for Robotics and Intelligent Systems Outline Introduction Granules JRoG features Incremental Feature Selection Method. Alan Yuille (UCLA & Korea University). . Leo Zhu. . (NYU/UCLA) & . Yuanhao Chen (UCLA). Y. Lin, C. Lin, Y. Lu (Microsoft Beijing). . . A. . . Torrabla. and W. . Freeman . (MIT). Peter Brook. Matei. . Ciocarlie. Kaijen. Hsiao. Uncertainty. in Object Perception. Sensed Scene. Real Scene. Collaborative Grasp Planning with Multiple Object Representations. 2. Uncertainty. in Object . Ross . Girshick. , Jeff Donahue, Trevor Darrell, . Jitandra. Malik (UC Berkeley). Presenter: . Hossein. . Azizpour. Abstract. Can CNN improve . s.o.a. . object detection results?. Yes, it helps by learning rich representations which can then be combined with computer vision techniques.. 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:. Ross Girshick. Microsoft Research. Guest lecture for UW CSE 455. Nov. 24, 2014. Outline. Object detection. the task, evaluation, datasets. Convolutional Neural Networks (CNNs). overview and history. Region-based Convolutional Networks (R-CNNs). 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. Alan Yuille (UCLA & Korea University). . Leo Zhu. . (NYU/UCLA) & . Yuanhao Chen (UCLA). Y. Lin, C. Lin, Y. Lu (Microsoft Beijing). . . A. . . Torrabla. and W. . Freeman . (MIT). Ross Girshick. Microsoft Research. Guest lecture for UW CSE 455. Nov. 24, 2014. Outline. Object detection. the task, evaluation, datasets. Convolutional Neural Networks (CNNs). overview and history. Region-based Convolutional Networks (R-CNNs). Ross Girshick. Microsoft Research. Guest lecture for UW CSE 455. Nov. 24, 2014. Outline. Object detection. the task, evaluation, datasets. Convolutional Neural Networks (CNNs). overview and history. Region-based Convolutional Networks (R-CNNs). HOGgles Visualizing Object Detection Features C. Vondrick , A. Khosla , T. Malisiewicz , A. Torralba ICCV , 2013 . presented by Ezgi Mercan Object Detection Failures Why do our detectors think water looks like a car? . for Robust Object Detection. Jiankang. Deng, . Shaoli. Huang, Jing Yang, . Hui. . Shuai. , . Zhengbo. Yu, . Zongguang. Lu, . Qiang. Ma, . Yali. Du, . Yi Wu. , . Qingshan. Liu, . Dacheng. Tao. 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. st. Half) Unit-II. . Ratna. . Biswas. Assistant Professor. . Vidyasagar. Teachers' Training College. COLLABORATIVE LEARNING. 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 .