PPT-SAS Deep Learning Object Detection, Keypoint Detection

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Xindian Long 201809 Outline Introduction Object Detection Concept and the YOLO Algorithm Object Detection Example CAS Action Facial Keypoint Detection Example DLPy

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SAS Deep Learning Object Detection, Keypoint Detection: Transcript

Xindian Long 201809 Outline Introduction Object Detection Concept and the YOLO Algorithm Object Detection Example CAS Action Facial Keypoint Detection Example DLPy Why SAS Deep Learning . 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.. 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). Presenter: . Yanming. . Guo. Adviser: Dr. Michael S. Lew. Deep learning. Human. Computer. 1:4. Human . v.s. . Computer. Deep learning. Human. Computer. 1:4. Human . v.s. . Computer. Deep Learning. Why better?. 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. 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). for . Facial Keypoint Detection. Maheen Rashid, Xiuye Gu, Yong Jae Lee. CVPR 2017. UC Davis. The Problem. Input. Output. Outline. Pain . Detection in Animals and . Humans. Interspecies T. ransfer . Learning for . 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? DistributedattackdetectionschemeusingdeeplearningapproachforInternetofThingsAbebeAbeshuDiro,NaveenChilamkurtiPII:S0167-739X(17)30848-8DOI:http://dx.doi.org/10.1016/j.future.2017.08.043Reference:FUTURE Salient KeypointPaper ID 1570232318Twenty Second National Conference on Communications NCC 2016Authors Prerana Mukherjee SiddharthSrivastava Brejesh LallDepartment of Electrical EngineeringIndian Ins Internet Research Initiative 2020-2021. Suraj Vathsa. Develop an Artificial Intelligence (Deep Learning) . enabled system to identify adventitious sounds such . as wheezes and crackles in audio recordings of . Commercially . available seizure detection systems suffer from unacceptably high false alarm rates. . Deep . learning algorithms, like Convolutional Neural Networks (CNNs), have not previously been effective due to the lack of big data resources. . Presented by Aditi . Kuchi. Supervisor: . Dr.. Md . Tamjidul. Hoque. 1. Presentation Overview. Sand boils – What, How, Why +Motivation. Dataset. Methods used & explanations, discussion. Viola-Jones’ algorithm (. 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.

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