PPT-Addressing Class Imbalance Problem in Semantic Segmentation using Binary Focal Loss

Rushikesh Chopade Aditya  Stanam University of Iowa amp Shrikant Pawar  Department of Geology and GeophysicsIndian Institute of Technology KharagpurKharagpur  West Bengal 721302 India . Shuai Zheng, Ming-Ming Cheng, Jonathan Warrell, Paul Sturgess, Vibhav Vineet, Carsten Rother*, Philip H. S. Torr. Torr Vision Group, University of Oxford. *The . Technische Universität . Dresden. Traditional Goal. By: A’laa . Kryeem. Lecturer: . Hagit. Hel-Or. What is . Segmentation from . Examples. ?. Segment an image based on one (or more) correctly segmented image(s) assumed to be from the same . domain. Anurag Arnab. Collaborators: . sadeep. . Jayasumana. , . shuai. . zheng. , Philip . torr. Introduction. Semantic Segmentation. Labelling every pixel in an image. A key part of Scene Understanding. Second-Order Pooling. João Carreira. 1,2. , Rui Caseiro. 1. , Jorge Batista. 1. , Cristian Sminchisescu. 2. 1. . Institute of Systems and Robotics. ,. . University of Coimbra. 2. . Faculty of Mathematics and Natural . recovery? . James Morris, PhD candidate, London South Bank University. Who are ‘harmful’ drinkers?. Score 16+ on the AUDIT (NICE CG115). Drinking 35+ (women) or 50+ units (men) per week (DoH 2009). Mostajabi. , . Yadollahpour. . and . Shakhnarovich. Toyota . Technological Institute at Chicago. Main Ideas. Casting semantic segmentation as classifying a set of . superpixels. .. Extracting CNN features from different levels of spatial context around the . Mostajabi. , . Yadollahpour. . and . Shakhnarovich. Toyota . Technological Institute at Chicago. Main Ideas. Casting semantic segmentation as classifying a set of . superpixels. .. Extracting CNN features from different levels of spatial context around the . Movement led by W3C that promotes common formats for data on the web. Describes things in a way that computer applications can understand it. Describes the relationship between things and properties of things. Paper by John McCormac, Ankur Handa, Andrew Davison, and Stefan Leutenegger Dyson Robotics Lab, Imperial College London. Presentation by Chris Conte. Hey robot, go fetch me a Twix from the snack bar. person. grass. trees. motorbike. road. Evaluation metric. Pixel classification!. Accuracy?. Heavily unbalanced. Common classes are over-emphasized. Intersection over Union. Average across classes and images. from the data leading to very promising segmentation results. In this work, we successfully used to segment and classify medical images for many years [9,10]. A CNN uses layers to transform the input The class P is the class that contains all the problems that are solved in polynomial time on the size of the input by a deterministic Turing Machine.. . For n being the size of the input, the running time is O(. Altered time for OH tomorrow: 9:00-10:00 am.. Please complete mid-semester feedback. Semantic Segmentation. The Task. person. grass. trees. motorbike. road. Evaluation metric. Pixel classification!. Accuracy?. Juan Carlos . Niebles. and Ranjay Krishna. Stanford Vision and Learning Lab. What we will learn today. Introduction to segmentation and clustering. Gestalt theory for perceptual grouping. Agglomerative clustering.