Large Scale Visual
Recognition Challenge (ILSVRC) 2013:
Classification spotlights
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
Use Patches
From:
Instead of
Patches From:
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
View 1:
View 2:
View 3:
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
CognitiveVision team
Cognitive Psychology Inspired
Image Classification using Deep
Neural Network
Kuiyuan Yang, Microsoft Research
Yalong Bai, Harbin Institute of Technology
Yong Rui, Microsoft Research
Our Classification Scheme
CognitiveVision team
Given a image, predict
its basic category firstly.
Basic Category
Classification
Dog
Easy to
distinguish
Predict sub
category
French
bulldog
Maltese
dog
Dog
Classification
…
Cat
dalmatian
tiger
cat
English
setter
Egyptian
cat
Siamese
cat
Cat
Classification
…
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:
Classification
Embedding
Detection
Your next
Application!
Publicly available at http://caffe.berkeleyvision.org/
Experiments for large scale visual recognition
+
We tried:
Deep CNN (following Krizhevsky et al’12)
Low level features &spatial granularities
Where did we fail?
top 1 acc = 0.567
Appliance and
instrument are
confusing for us,
including
- TV vs. Screen,
- Coffee mug vs. Cup,
- Flute vs. Microphone,
-…
Television (0.18)
Hair spray (0.18)
Coffee mug (0.10)
Flute (0.10)
Agenda
8:30 Classification&localization
8:50
9:20
9:05
9:35
9:50
Spotlights
10:30 Detection
10:50
11:10
11:30
Spotlights
11:40
Noon Discussion panel
14:00 Invited talk by Vittorio Ferrari:
Auto-annotation and self-assessment in ImageNet
14:40 Fine-Grained Challenge 2013
http://www.image-net.org/challenges/LSVRC/2013/iccv2013