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Multi Scale Recognition with DAG-CNNs
by S. Yang & D. Ramanan
March 22, 2016
Niladri Basu Bal (@ gmail.com)
Motivation for this paper?
1. Contemporary approaches: Input for classifier is extracted feature from last layer (i.e. only high level feature).
2. Through experiments it was found that:
a) Course classification (e.g. person/dog) works better with mid levelfeature information
b) Fine grained classification (e.g. model of car) works better with high level feature information
Outline
1. Overview of paper
2. Convolutional Neural Network comparison
3. Comparison of feature maps in different levels
4. DAG-CNNs
5. Experiments
6. Conclusion
Outline
1. Overview of paper
2. Convolutional Neural Network comparison
3. Comparison of feature maps in different levels
4. DAG-CNNs
5. Experiments
6. Conclusion
Overview of paper
1. Analyses existing pre-trained CNN models like Caffe and Deep19 .
2. Tries to find the correct type of layer whose feature map proves useful.
3. Designs a multi scale architecture (DAG-CNN) that takes advantage of all the high, middle, and low-level features.
4. The DAG-CNNs lowers the error rate of MIT67, Scene15, SUN397 (benchmark datasets).
Outline
1. Overview of paper
2. Convolutional Neural Network comparison
3. Comparison of feature maps in different levels
4. DAG-CNNs
5. Experiments
6. Conclusion
Convolutional Neural Networkcomparison
Source: Multi Scale Recognition with DAG-CNNs by Yang & Ramanan
Convolutional Neural Networkcomparison1. Single Scale CNN
a) Using the information of only the last layer .
b) It is a very simple structure .
2. Multi Scale CNN
a) Uses information from all previous layers
b) Learning is difficult , over-fitting is likely to occur which can be overcome by using ‘pooling’ (sum, avg, max).
Source: www.slideshare.net ; Multi Scale Recognition with DAG-CNNs by Daiki Yamamoto
Outline
1. Overview of paper
2. Convolutional Neural Network comparison
3. Comparison of feature maps in different levels
4. DAG-CNNs
5. Experiments
6. Conclusion
Comparison of feature maps in different levels1. Next figure is a image retrieval result
2. Caffe model and MIT67 dataset is used for analysis
3. The output of the resulting layer is classified by the Support Vector Machine (SVM).
4. Procedure image retrieval: K=7 (top 7) images with lowest Euclidian distance from the query is retrieved.
Results of image retrieval
Queries
Layer 11
Layer 20
Layer 11
Layer 20
Source: Multi Scale Recognition with DAG-CNNs by Yang & Ramanan
Classification of result accuracies using information for each layer
1. Next figure is an accuracy bar graph
2. Caffe model and MIT67 dataset is used for analysis
3. Features of each layer is sent to the SVM classifier separately and their accuracy is presented in the graph.
4. Notice the jump of accuracy at each ReLU layer.
Classification of result accuracies using information for each layer
Source: Multi Scale Recognition with DAG-CNNs by Yang & Ramanan
Map of the accuracy rate of the respective layers and classes
1. Next figure is a heat map of accuracy corresponding to each layer.
2. The classification was carried out for all classes (MIT67).
3. Classes are grouped together to show which layer information works best for them.
Map of the accuracy rate of the respective layers and classes
The most useful
Source: Multi Scale Recognition with DAG-CNNs by Yang & Ramanan
Outline
1. Overview of paper
2. Convolutional Neural Network comparison
3. Comparison of feature maps in different levels
4. DAG-CNNs
5. Experiments
6. Conclusion
DAG-CNNs (Directed acyclic graph)
1x1xF features 1x1xF features1x1xF features
F = number of classes
Source: Multi Scale Recognition with DAG-CNNs by Yang & Ramanan
Learning of DAG-CNNs
• The following formula is used at the classifier
input Label indicatorError function
Weight of Convolution
K = Layer number , x = learning data y = learning labelSource: www.slideshare.net ; Multi Scale Recognition
with DAG-CNNs by Daiki Yamamoto
Outline
1. Overview of paper
2. Convolutional Neural Network comparison
3. Comparison of feature maps in different levels
4. DAG-CNNs
5. Experiments
6. Conclusion
Experiments
1. Evaluation by Accuracy of classification data set was carried out .a) SUN397
• 100K image and 397 categories of landscape image
b) MIT67
• 15K images and 67 categories of indoor image
c) Scene15
• 2985 landscape image of indoor and outdoor
Evaluation
Source: Multi Scale Recognition with DAG-CNNs by Yang & Ramanan
Classification Result of MIT67
labels
Source: Multi Scale Recognition with DAG-CNNs by Yang & Ramanan
Outline
1. Overview of paper
2. Convolutional Neural Network comparison
3. Comparison of feature maps in different levels
4. DAG-CNNs
5. Experiments
6. Conclusion
Conclusion
1. Higher level features are not always best for classification.
2. The proposed DAG-CNN network is applicable for any existing single scale model (not just Caffe and Deep19).
3. Improved accuracy recorded in all the tests when DAG-CNN is used.
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