'Discriminative' presentation slideshows

ICCV 2005 Beijing, Short Course, Oct 15

ICCV 2005 Beijing, Short Course, Oct 15. Recognizing and Learning Object Categories. Li Fei-Fei, UIUC Rob Fergus, MIT Antonio Torralba, MIT. Agenda. Introduction Bag of words models Part-based models Discriminative methods Segmentation and recognition Conclusions. material thing.

638 views • 47 slides

CVPR 2007 Minneapolis, Short Course, June 17

CVPR 2007 Minneapolis, Short Course, June 17. Recognizing and Learning Object Categories: Year 2007. Li Fei-Fei, Princeton Rob Fergus, MIT Antonio Torralba, MIT. Agenda. Introduction Bag-of-words models Part-based models Discriminative methods Segmentation and recognition

473 views • 47 slides

Richer Human-Machine Communication in Attributes-based Visual Recognition

Richer Human-Machine Communication in Attributes-based Visual Recognition. Devi Parikh TTIC. Traditional Recognition. Dog. Chimpanzee. Tiger. ???. Attributes-based Recognition. Furry White. Black Big. Stripped Yellow. Stripped Black White Big. Dog. Chimpanzee. Tiger.

428 views • 34 slides

Conditional Random Fields: Probabilistic Models

Conditional Random Fields: Probabilistic Models. Pusan National University AILAB. Kim, Minho. x 1. x 2. x 3. X:. Birds. noun. like. verb. flowers. noun. Y:. y 1. y 2. y 3. Labeling Sequence Data Problem. X is a random variable over data sequences

337 views • 33 slides

Interactively Discovery of Attributes Vocabulary

Interactively Discovery of Attributes Vocabulary. Devi Parikh and Kristen Grauman. Traditional Recognition. Dog. Chimpanzee. Tiger. ???. Attributes-based Recognition. Furry White. Black Big. Stripped Yellow. Stripped Black White Big. Dog. Chimpanzee. Tiger. Applications.

382 views • 30 slides

Discriminative Structure and Parameter Learning for Markov Logic Networks

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Discriminative Structure and Parameter Learning for Markov Logic Networks

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910 views • 26 slides

ICCV 2009 Kyoto, Short Course, September 24

354 views • 24 slides

Faculty Evaluation The Second Most Important Chairly Task

Faculty Evaluation The Second Most Important Chairly Task. Greg Martin Department of Marketing and Economics Director, International Business Programs College of Business University of West Florida IAL Department Chairpersons Workshop June 12, 2007. What is It?.

377 views • 20 slides

Introduction

Introduction. Problem: Classifying attributes and actions in still images Model: Collection of part templates Specific scale space locations (human centric) Discriminative learning Sparse Activation. Motivation. Train. Test. Train. Test. Overview. Mining Parts &

249 views • 18 slides

Context for low-level saliency detection

Context for low-level saliency detection. Devi Parikh , Larry Zitnick and Tsuhan Chen. For what can context be used?. So far higher level tasks What about lower level tasks? Picking out salient (representative) patches in an image?. SVM. Sample image. Build histogram. Classify. ?.

343 views • 18 slides

Robust and Fast Collaborative Tracking with Two Stage Sparse Optimization

Robust and Fast Collaborative Tracking with Two Stage Sparse Optimization. Authors: Baiyang Liu, Lin Yang, Junzhou Huang , Peter Meer, Leiguang Gong and Casimir Kulikowski. Outline. Problem of Tracking State of the art algorithms The proposed algorithm Experiment result. The problem.

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270 views • 12 slides

Christopher M. Bishop

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LISTENING

LISTENING. Do you hear what I hear?. LISTENING. The deliberate, psychological process by which we receive, understand, and retain aural stimuli. HEARING VS LISTENING. HEARING IS A BIOLOGICAL ACTIVITY, BASED IN THE MECHANISMS OF THE EARS, PASSIVE IN NATURE.

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SELECT lab meeting: Jonathan Huang ( jch1@cs.cmu ) Advisor: Carlos Guestrin 4/25/2006

Location Based Activity Recognition Lin Liao, Dieter Fox, Henry Kautz. In Adv. in Neural Information Processing Systems, 2005. SELECT lab meeting: Jonathan Huang ( jch1@cs.cmu.edu ) Advisor: Carlos Guestrin 4/25/2006. Task. Given GPS location data we would like to:

275 views • 6 slides

Graph Classification

Graph Classification. SEG 5010 Week 3. A Summary of Graph Features. Fingerprint Maccs keys Tree and cyclic patterns Frequent subgraphs Graph fragments. A Boosting Approach to Graph Classification (NIPS04). Apply boosting to graph classification Weak learner: decision stump

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TITLE

TITLE. NAME MAJOR CONTACT MAIL ex( bjlee@bi.snu.ac.kr). RESULT. backgrounds . A a. A A A A A A Aa ?? ?? : . Objectives. A a. ?? 1. . SYSTEM OVERVIEW. RESULT GRAPH. RESULT CHART. ??? ??. a A A A A A. Summary. A A A A A. Experiment. A A A A A . .

121 views • 1 slides