PPT-1 Semantic Image Representation for Visual Recognition

Nikhil Rasiwasia Nuno Vasconcelos Statistical Visual Computing Laboratory University of California San Diego Thesis Defense Ill pause for a few moments so that you all can finish reading this . . Learning. for. . Word, Sense, Phrase, Document and Knowledge. Natural . Language Processing . Lab. , Tsinghua . University. Yu Zhao. , Xinxiong Chen, Yankai Lin, Yang Liu. Zhiyuan Liu. , Maosong Sun. Jay McClelland. Stanford University. The PDP Approach to Semantic Cognition. Distributed representation. Experience-driven learning. -> Development, adult performance, and effects of brain damage on semantic cognition. Summarizing Web Pages for Search and Revisitation. Jaime Teevan, Ed Cutrell, Danyel Fisher, Steven Drucker, . G. onzalo Ramos, Paul André. 1. , Chang Hu. 2. Microsoft Corporation. 1. University of Southampton . Piet Martens (Physics) & . Rafal. . Angryk. (CS). Montana State University. A Computer Science Approach to Image Recognition. Conundrum. : We can teach an undergraduate in ten minutes what a filament, sunspot, sigmoid, or bright point looks like, and have them build a catalog from a data series. Yet, teaching a computer the same is a very time consuming job – plus it remains just as demanding for every new feature.. . P . L . Chandrika. . . Advisors: Dr.. . C. V. Jawahar . . . Centre for Visual Information Technology, IIIT- Hyderabad. Problem Setting . . Michael Elad. The Computer Science Department. The Technion – Israel Institute of technology. Haifa 32000, Israel. MS45: Recent Advances in Sparse and . Non-local Image Regularization - Part III of III. Workshop on Broad-Coverage . Semantic . Analysis. University of Amsterdam. With thanks to: . Collaborators:. . Ming-Wei . Chang. ,. . Chritos. Christodoulopoulos. , . Dan . Goldwasser. ,. . Andrew Chi. Brian Cristante. COMP 790-133: January 27, 2015. Image Retrieval. AI / Vision Problem. Systems Design / Software Engineering Problem. Sensory Gap. : “What features should we use?”. Query-Dependent?. Analysis. . for Lexical Semantics . and . Knowledge Base Embedding. UIUC 2014 . Scott Wen-tau . Yih. Joint work with. Kai-Wei . Chang, Bishan Yang, . Chris Meek, Geoff Zweig, John Platt. Microsoft Research. Weihong Deng (. 邓伟洪. ). Beijing Univ. Post. & Telecom.(. 北京邮电大学. ) . 2. Characteristics of Face Pattern. The facial shapes are too similar, sometimes identical ! (~100% face detection rate, kinship verification). Linda Shapiro. CSE 455. 1. Face recognition: once you’ve detected and cropped a face, try to recognize it. Detection. Recognition. “Sally”. 2. Face recognition: overview. Typical scenario: few examples per face, identify or verify test example. Linda Shapiro. CSE 455. 1. Face recognition: once you’ve detected and cropped a face, try to recognize it. Detection. Recognition. “Sally”. 2. Face recognition: overview. Typical scenario: few examples per face, identify or verify test example. Source: . Charley Harper. Outline. Overview of recognition tasks. A statistical learning approach. “Classic” or “shallow” recognition pipeline. “Bag of features” representation. Classifiers: nearest neighbor, linear, SVM. Introduction. Semantic Role Labeling. Agent. Theme. Predicate. Location. Can we figure out that these have the same meaning?. XYZ . corporation . bought. the . stock.. They . sold. the stock to XYZ .