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Exploiting the Power of Deep
Learning with Constrained Data
Anthony S. Paek
Co-founder and CEO, Cldi Inc.
Donggeun Yoo
Ph.D Candidate, RCV Lab., KAIST
Scene classification.
Object detection.
Fine-grained classification.
Image retrieval.
Attribute recognition.
Domain transfer.
Neural activation of a pre-trained CNN
A generic image representation.
Neural activation of a pre-trained CNN
A generic image representation.
Neural activation of a pre-trained CNN
A generic image representation.
Parameter transfer.
Neural activation of a pre-trained CNN
A generic image representation.
Parameter transfer.
Target DB
Neural
activation.
Neural activation of a pre-trained CNN
A generic image representation.
Parameter transfer.
Target DB
Target
classifier.
Neural
activation.
Successful use of CNN activations.
Object classification on PASCAL VOC 2007.
[Oquab et al., CVPR 2014]
60.3%
77.70%
Object detection on PASCAL VOC 2007.
[Girshick et al., CVPR 2014]
34.30%
58.50%
Scene classification on MIT Indoor 67.
[Razavian et al., CVPRW 2014]
63.18%
68.24%
Fine-grained classification on Oxford 102 Flowers.
72.70%
86.80%
[Razavian et al., CVPRW 2014]
Image retrieval on Oxford 5k.
[Babenko et al., ECCV 2014]
44.80%
55.70%
How have CNN activations been used so far?
Single activation vector per image.
(Most of works.)
Pre-trained
CNN.
How have CNN activations been used so far?
Average pooling of multiple activations per image.
Pre-trained
CNN.
…
Average pooling.
How have been CNN activations used so far?
Single activation:
72.36% on PASCAL VOC 2007 classification
[Chatfield et al., BMVC 2014]
Marginal improvement.
Average pooling:
73.60% on PASCAL VOC 2007 classification.
Our strategy:
1. Abundant amount of local activation statistics.
2. State-of-the-art Fisher kernel framework.
[Perronnin et al., ECCV 2010]
…
Pre-trained
CNN.
…
Multi-scale
dense regions
Fisher kernel.
…
Fast extraction of multi-scale dense activations.
Idea:
FC layers can be implemented
with Conv layers.
227×227×3
FC 8
FC 7
FC 6
Conv. 5
Conv. 4
Conv. 3
Conv. 2
Conv. 1
322×322×3
FC 8
FC 7
FC 6
Conv. 5
Conv. 4
Conv. 3
Conv. 2
Conv. 1
Fast extraction of multi-scale dense activations.
Idea:
FC layers can be implemented
with Conv layers.
227×227×3
FC 8
FC 7
FC 6
Conv. 5
Conv. 4
Conv. 3
Conv. 2
Conv. 1
322×322×3
FC 8
Conv. 7
Conv. 6
Conv. 5
Conv. 4
Conv. 3
Conv. 2
Conv. 1
Fast extraction of multi-scale dense activations.
4,096
…
…
…
Multi-scale
dense
activations.
Fast extraction of multi-scale dense activations.
4,096
…
…
Each activation vector
comes from a single patch.
Multi-scale
dense
activations.
Fast extraction of multi-scale dense activations.
We can extract thousands of
rich activations (FC7) in a reasonable time.
CPU: 2.6GHz Intel Xeon.
GPU: GTX TITAN Black.
Pooling: Fisher kernel.
However, it does not show significant improvements.
Single activation: 72.36%
in VOC 2007.
This approach: 74.96%
Fisher
kernel.
…
7 scales.
Fisher
vector.
SVM.
Pooling: Fisher kernel.
Devil is in the scale property of CNN activation !
…
…
Image scale.
Pooling: Fisher kernel.
Devil is in the scale property of CNN activation !
Dense-SIFT well captures low-level texture
in small regions.
PASCAL VOC 2007
Pooling: Fisher kernel.
Devil is in the scale property of CNN activation !
CNN activation well captures
object parts or entire objects.
PASCAL VOC 2007
PASCAL VOC 2007
Solution:
Average pooling of
normalized Fisher vectors in each scale.
Fisher
kernel.
Fisher
kernel.
𝑙2 -norm.
Fisher
kernel.
𝑙2 -norm.
…
…
…
7 scales.
𝑙2 -norm.
Average
pooling.
Final representation.
Solution:
Average pooling of
normalized Fisher vectors in each scale.
It shows significant improvements in VOC 2007.
Single activation: 72.36%()()()()()()
Naïve Fisher: 74.96% (+2.60%)
Proposed method: 83.20% (+10.84%)
Solution:
Average pooling of
normalized Fisher vectors in each scale.
Evaluation
with three different visual recognition tasks.
Scene classification:
MIT Indoor 67.
Object classification:
PASCAL VOC 2007.
Fine-grained classification:
Oxford 102 Flowers.
Baselines and ours.
+7~18% to the single activation.
+6~15% to the average pooling.
State-of-the-art and ours
(MIT Indoor 67)
State-of-the-art and ours
(PASCAL VOC 2007)
FT: Fine-tuning CNN on VOC 2007.
BB: Use of bounding box annotation in training.
State-of-the-art and ours
(Oxford 102 Flowers)
One more thing:
Obtaining a reliable object-confidence map.
One more thing:
Obtaining a reliable object-confidence map.
1) A single local activation can be encoded to
a Fisher vector.
Fisher
kernel.
Fisher vector.
One more thing:
Obtaining a reliable object-confidence map.
1) A single local activation can be encoded to
a Fisher vector.
2) Each corresponding region is scored by
SVM weights trained for classification.
One more thing:
Obtaining a reliable object-confidence map.
1) A single local activation can be encoded to
a Fisher vector.
2) Each corresponding region is scored by
SVM weights trained for classification.
3) We vote each region to form an object
confidence map.
Examples in PASCAL VOC2007.
Note:
No bounding box annotations are used
in training.
Examples in PASCAL VOC2007.
Note:
No bounding box annotations are used
in training.
Examples in PASCAL VOC2007.
Note:
No bounding box annotations are used
in training.
Examples in PASCAL VOC2007.
Note:
No bounding box annotations are used
in training.
(This work)
(Coming up)
Classification
Detection
mAP: 83.2%
mAP: TBA
(State-of-the-art)
(State-of-the-art)
ICCV 2015 (submitted)
Anthony S. Paek
Co-founder and CEO, Cldi Inc.
[email protected]
Appendix
Experimental setup.
• Number of scales: 7.
[Shelhamer et al., 2014]
• Pre-trained CNN: Caffe reference model.
• Target activation layer: FC7.
• PCA dimension: 128.
• Number of Gaussians: 256.