1. Classification spotlights
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
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
Training neural networks can be quite time consuming, so the focus was on simple to test and simple to implement ideas to improve the convolutional neural network based image classification pipeline. Models perform better with more data so we added more training image patches by using all of the pixels from the image rather than only selecting training patches from the cropped central square. We also added additional color manipulations to these training image patches. At test time, we make predictions over multiple scales and multiple views of the image in order to generate diverse predictions and improve the overall combined prediction. Additionally, we build models on higher resolution images which can be quickly trained by fine tuning previously trained models. The final system achieves a 13.6% top five error rate using 5 base models and 5 high resolution models and these new additions to the pipeline.
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

3. 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

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

5. Publicly available at http://caffe.berkeleyvision.org/
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:
Your next
Application!
Classification Embedding Detection
Publicly available at

6. 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)

7. Agenda 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
8:50
9:20
9:35
9:50
Spotlights
9:05
10:50
11:10
11:30
Spotlights
11:40