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Large Scale Visual Recognition Challenge (ILSVRC) 2013: Classification spotlights.

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Presentation on theme: "Large Scale Visual Recognition Challenge (ILSVRC) 2013: Classification spotlights."— Presentation transcript:

1 Large Scale Visual Recognition Challenge (ILSVRC) 2013: Classification spotlights

2 Additions to the ConvNet Image Classification Pipeline Andrew Howard – Andrew Howard Consulting Changes to Training: Use more pixels: Train on square patches from rectangular image instead of cropped central square Additional color manipulation of contrast, brightness, color balance used on training patches Changes to Testing: Make Predictions at different scales and different views which use all pixels Previous: Used 10 predictions (2 flips * 5 translations) This Submission: Used 90 predictions (2 flips * 5 translations * 3 scales * 3 views) The number of predictions can be reduced with no loss of accuracy with stagewise regression Higher Resolution Models: Use a fully trained model and fine tune on image patches from a higher resolution image This can be trained in about 1/3 the number of epochs Predictions on higher resolution images give complimentary predictions to the base model Final Vision System achieves 13.6% error and is made of 5 base models and 5 higher resolution models Structure is the same as last year with fully connected layers twice as large, which doesn’t add much value Use Patches From: Instead of Patches From: View 1:View 2:View 3:

3 Cognitive Psychology Inspired Image Classification using Deep Neural Network Kuiyuan Yang, Microsoft Research Yalong Bai, Harbin Institute of Technology Yong Rui, Microsoft Research CognitiveVision team

4 Our Classification Scheme Dog Cat French bulldog English setter Maltese dog Basic Category Classification Easy to distinguish Dog Classification Given a image, predict its basic category firstly. … Egyptian cat Siamese cat tiger cat Cat Classification dalmatian … Predict sub category CognitiveVision team

5 Caffe: Open-Sourcing Deep Learning Yangqing Jia, Trevor Darrell, UC Berkeley Convolutional Architecture for Fast Feature Extraction – Seamless switching between CPU and GPU – Fast computation (2.5ms / image with GPU) – Full training and testing capability – Reference ImageNet model available A framework to support multiple applications: Publicly available at Classification Embedding Detection Your next Application!

6 Experiments for large scale visual recognition Deep CNN (following Krizhevsky et al’12) We tried: + Low level features &spatial granularities Where did we fail? Television (0.18)Hair spray (0.18)Coffee mug (0.10) Flute (0.10) - TV vs. Screen, - Coffee mug vs. Cup, - Flute vs. Microphone, - … top 1 acc = 0.567 Appliance and instrument are confusing for us, including

7 8:30 Classification&localization 10:30 Detection Noon Discussion panel 14:00 Invited talk by Vittorio Ferrari: Auto-annotation and self-assessment in ImageNet 14:40 Fine-Grained Challenge 2013 Agenda 8:50 9:05 9:20 9:35 9:50 Spotlights 10:50 11:10 11:30 11:40 Spotlights

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