PPT-CNN for No-Reference Image Quality Assessment(NR-IQA)

Tzachi Hershkovich Image Quality Degradation sources Full ReferenceImage Quality Assessment vs No ReferenceImage Quality Assessment System architecture Training Evaluation and results. By Zhang . Liliang. Main idea: good features are no enough. VOC07: mAP:35.1. % -> 58.5%. Overview. (1) the model of R-CNN. (2) the result of R-CNN. (3) some discussions. Visualizing learned feature in CNN. 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. . hongliang. . xue. Motivation. . Face recognition technology is widely used in our lives. . Using MATLAB. . ORL database. Database. The ORL Database of Faces. taken between April 1992 and April 1994 at the Cambridge University Computer . Yunchao. Wei, Wei Xia, . Junshi. Huang, . Bingbing. Ni, Jian Dong, Yao Zhao, Senior Member, IEEE . Shuicheng. Yan, Senior Member, IEEE. 2014. . arXiv. IEEE. . Short Papers. . HCPIssue. Date: Sept. 1 2016. Deformable Part Models with CNN Features. Pierre-André . Savalle. , . Stavros . Tsogkas. , George Papandreou, Iasonas Kokkinos. From HOG to CNN features. Detection . performance of C-DPM. Method. . Lin Ma, . Zhengdong. Lu, and Hang Li. Huawei Noah’s Ark Lab, Hong Kong. http://. www.ee.cuhk.edu.hk. /~lma. /. . Mine the relationships between multiple modalities. Association different modalities. 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. Xueying. Bai, . Jiankun. Xu. Multi-label Image Classification. Co-occurrence dependency. Higher-order correlation: one label can be predicted using the previous label. Semantic redundancy: labels have overlapping meanings (cat and kitten). Deep Learning Architectures. feed-forward . networks. auto-encoders (output want to recover input image, middle layer smaller - use results of middle layer for compression. ). recurrent neural networks (RNNs) (backward feeding at run time as part of input into middle . person 1. person 2. horse 1. horse 2. R-CNN: Regions with CNN features. Input. image. Extract region. proposals (~2k / image). Compute CNN. features. Classify regions. (linear SVM). Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation. CNN 10. August 4, 2018. Landmark Museum Lost. U.S. Supreme Court Confirmation Hearings . School Water Fountains Shut Off . Positive Athlete Shows Exceptional Perseverance. Make Up Day. September 4, 2018. Convolutional Neural Networks. Spring 2018. CS 599.. Instructor: Jyo Deshmukh. Neural network basics. Convolutional Neural Nets. Layout. 2. A feedforward neural network with . hidden layers is defined as follows:. Deep Learning for Expression Recognition in Image Sequences Daniel Natanael García Zapata Tutors: Dr. Sergio Escalera Dr. Gholamreza Anbarjafari April 27 2018 Introduction and Goals Introduction Dennis Hamester et al., “Face ExpressionRecognition with a 2-Channel ConvolutionalNeural Network”, International Joint Conference on Neural Networks (IJCNN), 2015. Paper ID: 8762. K. M. Naimul Hassan. , Md. Shamiul . Alam. . Hridoy. , Naima Tasnim, . Atia. . Faria. Chowdhury, Tanvir . Alam. Roni, Sheikh Tabrez, Arik . Subhana. , Celia Shahnaz. Department of Electrical and Electronic Engineering (EEE),.