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REGION-ORIENTED CONVOLUTIONAL
NETWORKS FOR OBJECT RETRIEVAL
Eduard Fontdevila Amaia Salvador Xavier Giró-i-Nieto
ADVISORSAUTHOR
ACKNOWLEDGEMENTS
2
Financial Support Technical Support
Albert Gil Josep Pujal
OUTLINE
1. Motivation
2. State of Art
3. Local CNNs for Instance Search
4. Fine-tuning
5. Conclusions
3
visual Data is Big Data
4
motivation
libraries need librarians...
5
motivation
... and visual Data needs Computer Vision
6
COMPUTER
VISION
motivation
applications
7
motivation
...
OUTLINE
1. Motivation
2. State of Art
3. Local CNNs for Instance Search
4. Fine-tuning
5. Conclusions
8
from shallow to deep learning
9
Bag of Words
SIFT
Histograms of gradients
Convolutional Neural Networks (CNNs)
“hand crafted” features
state of art
“learned” features
why deep learning now?
10
state of art
large datasets Powerful GPUs
...
AlexNet
11
state of art
Krizhevsky et al. (Toronto), ImageNet Classification with Deep Convolutional Neural Networks (2012)
CaffeNet
12
state of art
CaffeNet
architecture
[Krizhevsky’12]
data
[Deng’09]
framework
[Jia’14]
Slide credit: Xavier Giró-i-Nieto
CaffeNet
13
state of art
input
image
Babenko et al. (Moskow), Neural Codes for Image Retrieval (2014)
CaffeNet
14
state of art
convolutional layers
Babenko et al. (Moskow), Neural Codes for Image Retrieval (2014)
CaffeNet
15
state of art
fully connected
layers
Babenko et al. (Moskow), Neural Codes for Image Retrieval (2014)
object candidates
16
state of art
Selective Search bounding boxes
Uijlings et al. (Trento), Selective Search for Object Recognition (2013)
MCG segments
Arbeláez et al. (Berkeley), Multiscale Combinatorial Grouping (2014)
R-CNN
17
state of art
Girshick et al. (Berkeley), Rich feature hierarchies for accurate object detection and semantic segmentation (2014)
Object Detection network
fast R-CNN
18
state of art
R. Girshick (Berkeley), Fast R-CNN (2015)
SDS
19
state of art
Hariharan et al. (Berkeley), Simultaneous Detection and Segmentation (2014)
Object Detection + Semantic Segmentation network
OUTLINE
1. Motivation
2. State of Art
3. Local CNNs for Instance Search
4. Fine-tuning
5. Conclusions
20
TRECVid Instance Search
21
local CNNs for instance search
large collection of videos
464h
shots
~470k
frames
1/4 fps
TRECVid Instance Search
22
local CNNs for instance search
large collection of videos
464h
shots
~470k
frames
1/4 fps
...in our case, subset of 13k shots (23k frames)
a Big Data scenario
23
local CNNs for instance search
query descriptors
24
local CNNs for instance search
CaffeNet
Fast R-CNN
SDS
visual features
visual features
visual features
query set
descriptors
image
bbox
region
query descriptors
25
local CNNs for instance search
query set
examples of TRECVid query images
query descriptors
26
local CNNs for instance search
CaffeNet
Fast R-CNN
SDS
visual features
visual features
visual features
query set
descriptors
image
bbox
region
object
candidates
main scheme
27
local CNNs for instance search
CaffeNet
Fast R-CNN
SDS
visual
features
visual
features
visual
features
query
descriptors
matching
matching
matching
frames
in 1 shot
pooling
pooling
pooling
ranking
ranking
ranking
object
candidates
pooling
pooling
visual
features
visual
features
main scheme
28
local CNNs for instance search
CaffeNet
Fast R-CNN
SDS
visual
features
query
descriptors
matching
matching
matching
frames
in 1 shot
pooling ranking
ranking
ranking
global approach
poolingvisual
features
object
candidates
pooling
pooling
visual
features
visual
features
main scheme
29
local CNNs for instance search
CaffeNet
Fast R-CNN
SDS
query
descriptors
matching
matching
matching
frames
in 1 shot
ranking
ranking
ranking
global approach
visual
features
pooling
object
candidates
pooling
pooling
visual
features
visual
features
main scheme
30
local CNNs for instance search
CaffeNet
Fast R-CNN
SDS
query
descriptors
matching
matching
matching
frames
in 1 shot
ranking
ranking
ranking
global approach
visual
features
pooling
object
candidates
pooling
pooling
visual
features
visual
features
main scheme
31
local CNNs for instance search
CaffeNet
Fast R-CNN
SDS
query
descriptors
matching
frames
in 1 shot matching
matching ranking
ranking
ranking
global approach
euclidean distance
Babenko et al. (Moskow), Neural Codes for Image Retrieval (2014)
poolingvisual
features
object
candidates
pooling
pooling
visual
features
visual
features
main scheme
32
local CNNs for instance search
CaffeNet
Fast R-CNN
SDS
query
descriptors
matching
matching
matching
frames
in 1 shot
ranking
ranking
ranking
global approach
Zhu et al. (NII), Multi-image aggregation for better visual object retrieval (2014)
distance
frame 1
distance
frame 2
distance
frame 3
average distance
distance shot - query
=
poolingvisual
features
object
candidates
pooling
pooling
visual
features
visual
features
main scheme
33
local CNNs for instance search
CaffeNet
Fast R-CNN
SDS
query
descriptors
matching
matching
matching
frames
in 1 shot
ranking
ranking
ranking
global approach
only top1000 shots
object
candidates
main scheme
34
local CNNs for instance search
CaffeNet
Fast R-CNN
SDS
visual
features
visual
features
visual
features
query
descriptors
matching
matching
matching
frames
in 1 shot
pooling
pooling
pooling
ranking
ranking
ranking
visual
features
pooling
object
candidates
main scheme
35
local CNNs for instance search
CaffeNet
Fast R-CNN
SDS
visual
features
visual
features
query
descriptors
matching
matching
matching
pooling
pooling
ranking
ranking
ranking
local approach
frames
in 1 shot
object
candidates
main scheme
36
local CNNs for instance search
CaffeNet
Fast R-CNN
SDS
frames
in 1 shot
local approach
visual
features
pooling
object
candidates
main scheme
37
local CNNs for instance search
CaffeNet
Fast R-CNN
SDS
visual
features
visual
features
query
descriptors
matching
matching
matching
pooling
pooling
ranking
ranking
ranking
local approach
frames
in 1 shot
quantitative results: ranking
38
local CNNs for instance search
mAP (%)
SDS Fast R-CNN
re-ranking
39
local CNNs for instance search
CaffeNet SDS / F-RCNN re-ranking
global + local
fusion
quantitative results: re-ranking
40
mAP (%)
SDS Fast R-CNN CaffeNet
local CNNs for instance search
quantitative results: re-ranking
41
mAP (%)
SDS Fast R-CNN CaffeNet
local CNNs for instance search
adding context
~8%
qualitative results: re-ranking
42
query
SDS
Fast R-CNN
local CNNs for instance search
qualitative results: re-ranking
43
query
SDS
Fast R-CNN
local CNNs for instance search
as a reminder...
44
local CNNs for instance search
Selective Search bounding boxes
Uijlings et al. (Trento), Selective Search for Object Recognition (2013)
MCG segments
Arbeláez et al. (Berkeley), Multiscale Combinatorial Grouping (2014)
Fast R-CNN
SDS
OUTLINE
1. Motivation
2. State of Art
3. Local CNNs for Instance Search
4. Fine-tuning
5. Conclusions
45
training CNNs from scratch is costly...
46
fine-tuning
... instead: fine-tuning
47
fine-tuning
already trained network new dataset (novel domain)
resume training
a quick trial
48
fine-tuning
CaffeNet Pascal dataset
results on Pascal (global scale)
49
fine-tuning
validation
subset
validation
set
accuracy (%) 59,31% 4,14%
Histogram of images per category
categories
%ofimages
Microsoft COCO
50
fine-tuning
● Multiple objects per image
● 80 categories
● > 300k images (80k training)
● > 2M instances
Lin et al. (Cornell - Microsoft), http://vision.ucsd.edu/sites/default/files/coco_eccv.pdf (2015)
fine-tuning SDS on COCO
51
fine-tuning
SDS network COCO dataset
resume training
fine-tuning SDS on COCO
52
fine-tuning
SDS network COCO dataset
resume training
... but why?
53
fine-tuning
the more objects the network knows, the better
OUTLINE
1. Motivation
2. State of Art
3. Local CNNs for Instance Search
4. Fine-tuning
5. Conclusions
54
about the results
● Although not outperforming CaffeNet: SDS good for localization!
55
conclusions
maybe more suitable for TRECVid localization task?
about fine-tuning
● Networks trained on objects, but not on the objects to retrieve
56
conclusions
fine-tuning on a larger dataset is clearly the next step
about object candidates
● Only 100 candidates decreseases likelihood to success
... but using a higher number
57
conclusions
Fast SDS would be the key
thank you
visualizing CNNs’ features
more class-specific information
annex
SDS: Proposal Generation
input image
MCG object candidates
segments, not only bounding boxes
annex
SDS: Feature Extraction
annex
SDS: Feature Extraction
object candidate
penultimate fully connected layers
annex
SDS: Region Classification
Linear SVM
annex
SDS: Region Refinement
annex
basic pipeline for retrieval
annex
interactive: Multi-image aggregation
Query images for a topic was used with the min distance to each shot.
The best option with SIFT-BoW is average, wheteher features (Avg-Pooling) or similarity scores (Sim-Avg)
annex
Zhu et al. (NII), Multi-image aggregation for better visual object retrieval (2014)

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