PPT-CNN-RNN: A Unified Framework for Multi-label Image Classification

Xueying Bai Jiankun Xu Multilabel Image Classification Cooccurrence dependency Higherorder correlation one label can be predicted using the previous label Semantic redundancy labels have overlapping meanings cat and kitten. 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. Socher. , Bauer, Manning, NG 2013. Problem. How can we parse a sentence and create a dense representation of it? . N-grams have obvious . problems, most important is . sparsity. Can we resolve syntactic ambiguity with context? “They ate . Abhishek Narwekar, Anusri Pampari. CS 598: Deep Learning and Recognition, Fall 2016. Lecture Outline. Introduction. Learning Long Term Dependencies. Regularization. Visualization for RNNs. Section 1: Introduction. . 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. . Tzachi. . Hershkovich. Image Quality – Degradation sources. Full Reference-Image Quality Assessment vs. No . Reference-Image Quality Assessment. System architecture. Training. Evaluation and results. Example Application. Slot Filling. I would like to arrive . Taipei . on . November 2. nd. .. . ticket booking system. Destination:. time of arrival:. Taipei. November 2. nd. . Slot. Example Application. Abhishek Narwekar, Anusri Pampari. CS 598: Deep Learning and Recognition, Fall 2016. Lecture Outline. Introduction. Learning Long Term Dependencies. Regularization. Visualization for RNNs. Section 1: Introduction. By Blake Ellis and Melanie Hicken, Senior Writers . Email us at watchdog@cnn.com. watchdog@cnn.com. 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. Machine l earning - based i ttH → inv isible Xubo GU @ Shanghai Jiao Tong University , School of Mechanical Engineering CERN Work Project Report CERN, CMS Supervisor s : Benjamin Krikler , Oli 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),.