Learning visual representations
for unfamiliar environments
Kate Saenko, Brian Kulis,
Trevor Darrell
UC Berkeley EECS & ICSI
The challenge of large scale visual interaction
Last decade has proven the superiority of models
learned from data vs. hand engineered structures!
Large-scale learning
• “Unsupervised”: Learn models from “found data”;
often exploit multiple modalities (text+image)
… The Tote is the perfect example of
two handbag design principles that ...
The lines of this tote are incredibly
sleek, but ... The semi buckles that
form the handle attachments are ...
E.g., finding visual senses
Artifact sense: “telephone”
DICTIONARY
1: (n) telephone, phone,
telephone set (electronic
equipment that converts
sound into electrical
signals that can be
transmitted over distances
and then converts received
signals back into sounds)
2: (n) telephone,
telephony (transmitting
speech at a distance)
[Saenko and Darrell ’09]
4
Large-scale Learning
• “Unsupervised”: Learn models from “found data”;
often exploit multiple modalities (text+image)
… The Tote is the perfect example of
two handbag design principles that ...
The lines of this tote are incredibly
sleek, but ... The semi buckles that
form the handle attachments are ...
• Supervised: Crowdsource labels (e.g., ImageNet)
Yet…
• Even the best collection of images from the web and
strong machine learning methods can often yield poor
classifiers on in-situ data!
?
• Supervised learning assumption: training distribution
== test distribution
• Unsupervised learning assumption: joint distribution is
stationary w.r.t. online world and real world
Almost never true!
6
“What You Saw Is Not What You Get”
SVM:54%
NBNN:61%
SVM:20%
NBNN:19%
The models fail due to domain shift
Examples of visual domain shifts
digital SLR
amazon.com
webcam
Consumer images
Close-up
FLICKR
Far-away
CCTV
Examples of domain shift:
change in camera, feature type, dimension
digital SLR
webcam
SURF
SIFT
VQ to 300
Different
dimensions
VQ to
1000
Solutions?
• Do nothing (poor performance)
• Collect all types of data (impossible)
• Find out what changed (impractical)
• Learn what changed
Prior Work on Domain Adaptation
• Pre-process the data [Daumé ’07] : replicate
features to also create source- and domainspecific versions; re-train learner on new features
• SVM-based methods [Yang’07], [Jiang’08],
[Duan’09], [Duan’10] : adapt SVM parameters
• Kernel mean matching [Gretton’09] : re-weight
training data to match test data distribution
Our paradigm: Transform-based
Domain Adaptation
Previous methods’ drawbacks
Example: “green” and “blue” domains
• cannot transfer learned shift
to new categories
• cannot handle new features
We can do both by learning
domain transformations*
* Saenko, Kulis, Fritz, and Darrell.
Adapting visual category models to
new domains. ECCV, 2010
W
Limitations of symmetric transforms
Symmetric assumption fails!
Saenko et al. ECCV10 used
metric learning:
• symmetric transforms
• same features
How do we learn more
general shifts?
W
Latest approach*: asymmetric transforms
Asymmetric transform (rotation)
• Metric learning model no
longer applicable
• We propose to learn
asymmetric transforms
– Map from target to source
– Handle different dimensions
*Kulis, Saenko, and Darrell, What You
Saw is Not What You Get: Domain
Adaptation Using Asymmetric Kernel
Transforms, CVPR 2011
Latest approach: asymmetric transforms
Asymmetric transform (rotation)
• Metric learning model no
longer applicable
• We propose to learn
asymmetric transforms
– Map from target to source
– Handle different dimensions
W
Model Details
W
• Learn a linear transformation to map points
from one domain to another
– Call this transformation W
– Matrices of source and target:
Loss Functions
Choose a point x from the
source and y from the
target, and consider inner
product:
Should be “large” for similar
objects and “small” for dissimilar
objects
Loss Functions
• Input to problem includes a collection of m
loss functions
• General assumption: loss functions depend
on data only through inner product matrix
Regularized Objective Function
• Minimize a linear combination of sum of loss
functions and a regularizer:
• We use squared Frobenius norm as a
regularizer
– Not restricted to this choice
The Model Has Drawbacks
• A linear transformation may be insufficient
• Cost of optimization grows as the product of
the dimensionalities of the source and target
data
• What to do?
Kernelization
• Main idea: run in kernel space
– Use a non-linear kernel function (e.g., RBF kernel)
to learn non-linear transformations in input space
– Resulting optimization is independent of input
dimensionality
– Additional assumption necessary: regularizer is a
spectral function
Kernelization
Kernel matrices for source
and target
Original Transformation
Learning Problem
New Kernel Problem
Relationship between
original and new problems
at optimality
Summary of approach
Input
space
Input
space
1. Multi-Domain Data
2. Generate Constraints, Learn W
Test point
y1
3. Map via W
y2
Test point
4. Apply to New Categories
Multi-domain dataset
Experimental Setup
• Utilized a standard bag-of-words model
• Also utilize different features in the target domain
– SURF vs SIFT
– Different visual word dictionaries
• Baseline for comparing such data: KCCA
Novel-class experiments
Our Method (linear)
Our Method
• Test method’s ability to transfer domain shift to unseen
classes
• Train transform on half of the classes, test on the other half
Extreme shift example
Query from target
Nearest neighbors in source using KCCA+KNN
Nearest neighbors in source using transformation
Conclusion
• Should not rely on hand-engineered features any
more than we rely on hand engineered models!
• Learn feature transformation across domains
• Developed a domain adaptation method based on
regularized non-linear transforms
– Asymmetric transform achieves best results on more
extreme shifts
– Saenko et al ECCV 2010 and Kulis et al CVPR 2011;
journal version forthcoming