Large-Scale Object Recognition using Label Relation Graphs

Jia Deng1,2, Nan Ding2, Yangqing Jia2, Andrea Frome2, Kevin Murphy2, Samy Bengio2, Yuan Li2, Hartmut Neven2, Hartwig Adam2

University of Michigan1, Google2

Object Classification

• Assign semantic labels to objects

CorgiPuppy

Dog

Cat

✔

✔

✖

✔

Object Classification

• Assign semantic labels to objects

Probabilities

0.9

0.8

0.9

0.1

CorgiPuppy

Dog

Cat

Object Classification

• Assign semantic labels to objects

Feature Extractor

Features Classifier Probabilities

0.9

0.8

0.9

0.1

CorgiPuppy

Dog

Cat

Object Classification

• Multiclass classifier: Softmax

CorgiPuppy

Dog

Cat

/

/

/

/

+

Assumes mutual exclusive labels.

0.2

0.4

0.3

0.1

• Independent binary classifiers: Logistic Regression

CorgiPuppy

Dog

Cat

0.2

0.8

0.6

0.4

No assumptions about relations.

Object labels have rich relations

Corgi Puppy

Dog Cat

ExclusionHierarchical

Dog

CatCorgi Puppy

OverlapSoftmax: all labels are mutually exclusive Logistic Regression: all labels overlap

Goal: A new classification model

Respects real world label relations

CorgiPuppy

Dog

Cat

0.9

0.8

0.9

0.1Corgi Puppy

Dog Cat

Visual Model + Knowledge Graph

CorgiPuppy

Dog

Cat

Visual Model

0.9

0.8

0.9

0.1

Knowledge Graph

Joint Inference

Assumption in this work:Knowledge graph is given and fixed.

Agenda

• Encoding prior knowledge (HEX graph)• Classification model• Efficient Exact Inference• Experiments• Conclusion and Future Work

Agenda

• Encoding prior knowledge (HEX graph)• Classification model• Efficient Exact Inference• Experiments• Conclusion and Future Work

Hierarchy and Exclusion (HEX) Graph

Corgi Puppy

Dog Cat

ExclusionHierarchical

• Hierarchical edges (directed)• Exclusion edges (undirected)

Examples of HEX graphs

Car Bird

Dog Cat

Male Female

Person

Child

BoyRound

Red Shiny

Thick

Mutually exclusive All overlapping Combination

Girl

State Space: Legal label configurations

Dog Cat Corgi Puppy

0 0 0 0

0 0 0 1

0 0 1 0

0 0 1 1

1 0 0 0

…

1 1 0 0

1 1 0 1

…

Corgi Puppy

Dog Cat

Each edge defines a constraint.

State Space: Legal label configurations

Corgi Puppy

Dog Cat Dog Cat Corgi Puppy

0 0 0 0

0 0 0 1

0 0 1 0

0 0 1 1

1 0 0 0

…

1 1 0 0

1 1 0 1

…

Hierarchy: (dog, corgi) can’t be (0,1)

Each edge defines a constraint.

State Space: Legal label configurations

Corgi Puppy

Dog Cat Dog Cat Corgi Puppy

0 0 0 0

0 0 0 1

0 0 1 0

0 0 1 1

1 0 0 0

…

1 1 0 0

1 1 0 1

…Exclusion: (dog, cat) can’t be (1,1)

Hierarchy: (dog, corgi) can’t be (0,1)

Each edge defines a constraint.

Agenda

• Encoding prior knowledge (HEX graph)• Classification model• Efficient Exact Inference• Experiments• Conclusion and Future Work

HEX Classification Model• Pairwise Conditional Random Field (CRF)

Input scores Binary Label vector

HEX Classification Model• Pairwise Conditional Random Field (CRF)

Binary Label vector

Unary: same as logistic regression

Input scores

HEX Classification Model• Pairwise Conditional Random Field (CRF)

Binary Label vector

All illegal configurations have probability zero.

Unary: same as logistic regression

If violates constraints

Otherwise

0

Pairwise: set illegal configuration to zero

Input scores

HEX Classification Model• Pairwise Conditional Random Field (CRF)

Binary Label vector

Partition function: Sum over all (legal) configurations

Input scores

HEX Classification Model• Pairwise Conditional Random Field (CRF)

Binary Label vector

Probability of a single label: marginalize all other labels.

Input scores

Special Case of HEX Model

• Softmax

Car Bird

Dog Cat

Round

Red Shiny

Mutually exclusive All overlapping

Thick

• Logistic Regressions

Learning

CorgiPuppy

Dog

CatDNN

Label: Dog

Maximize marginal probability of observed labels

Back Propagation

DogCorgi

Puppy

Cat

1?

?

?

Agenda

• Encoding prior knowledge (HEX graph)• Classification model• Efficient Exact Inference• Experiments• Conclusion

Naïve Exact Inference is Intractable• Inference: – Computing partition function– Perform marginalization

• HEX-CRF can be densely connected (large treewidth)

Observation 1: Exclusions are good

Car Bird

Dog Cat

• Lots of exclusions Small state space Efficient inference• Realistic graphs have lots of exclusions.• Rigorous analysis in paper.

Number of legal states is O(n), not O(2n).

Observation 2: Equivalent graphs

Dog Cat

Corgi

PuppyPembroke Welsh Corgi

Cardigan Welsh Corgi

Dog Cat

Corgi

PuppyPembroke Welsh Corgi

Cardigan Welsh Corgi

Observation 2: Equivalent graphs

Sparse equivalent• Small Treewidth • Dynamic programming

Dog Cat

Corgi

PuppyPembroke Welsh Corgi

Cardigan Welsh Corgi

Dog Cat

Corgi

PuppyPembroke Welsh Corgi

Cardigan Welsh Corgi

Dog Cat

Corgi

PuppyPembroke Welsh Corgi

Cardigan Welsh Corgi

Dense equivalent• Prune states • Can brute force

A

BF

B

ED

C

G

F

BC

F

HEX Graph Inference

A

B

E

D

C

G

F

A

B

E

D

C

G

F

A

B

E

D

C

G

F

A

BF

B

ED

C

G

F

BC

F

1. Sparsify

(offline)

3.Densify(offline)

2.Build Junction Tree(offline)

4.Prune Clique States

(offline)

5. Message Passing on

legal states (online)

Agenda

• Encoding prior knowledge (HEX graph)• Classification model• Efficient Exact Inference• Experiments• Conclusion and Future Work

Exp 1: Learning with weak labels• Many basic category labels• Few fine-grained labels

Dog

Corgi

Animal

……

Weak labels:No information on subcategories.

CorgiPuppy

Dog

CatDNN

Label: Dog

DogCorgi

Puppy

Cat

1?

?

?

Hypothesis: HEX models can improve fine-grained recognition using basic level labels.

Exp 1: Learning with weak labels

• ILSVRC 2012: “relabel” or “weaken” a portion of fine-grained leaf labels to basic level labels.

• Evaluate on fine-grained recognition

Exp 1: Learning with weak labels

Dog

Corgi

Animal

…

Husky

Dog

Corgi

Animal…

Husky

Relabel

Training(“weakened” labels)

Test

Dog

Corgi

Animal

…

Husky

Original ILSVRC 2012 (leaf labels)

• ILSVRC 2012: “relabel” or “weaken” a portion of fine-grained leaf labels to basic level labels.

• Evaluate on fine-grained recognition.• Consistently outperforms baselines.

Exp 1: Learning with weak labels

Top 1 accuracy (top 5 accuracy)

Exp 2: Zero-Shot Recognition using Object-Attribute Knowledge

• Animals with Attribute (AwA) dataset (Lampert et al. 2009)• Training: • Observe only a subset of animal labels. • Given all animal-attribute relations • Indirectly learns attributes.

• Test: predict new classes with no images in training.

DAP (Lampert et al.) IAP (Lampert et al.) Ours

40.5% 27.8% 38.5%

polar bear

black: nowhite: yesbrown: nostripes: no

polar bearblack

white

zebrabrown

stripes

zebra

black: yeswhite: yesbrown: nostripes: yes … …

Related Work• Multilabel Annotation & Hierarchy

[Lampert et al. NIPS’11][Chen et al. ICCV’11][Bi & Kwok, NIPS’12][Bucak et al. CVPR’11]Ours: Unifies hierarchy and exclusion.

• Transfer learning & Attributes[Rohrbach et al. CVPR’10][Lampert et al. CVPR’09][Kuettel et al. ECCV’12][Akata et al. CVPR’13]Ours: A classification model that allows transferring.

• Extracting Common Sense Knowledge[Chen et al. ICCV’13][Zitnick & Parikh CVPR’13]Ours: Assumes knowledge is given.

[Hwang et al. CVPR’11][Kang et al. CVPR’06][Marszalek & Schmid CVPR’07][Zweig & Weinshall CVPR’07]

[Farhadi et al. CVPR’10][Lim et al. NIPS’11][Yu et al. CVPR’13][Fergus et al. ECCV’10]

[Zhu et al. ECCV’14][Fouhey & Zitnick CVPR’14]

Visual Model

External Knowledge

Conclusions

• A unified framework for single object classification– Generalizes standard classification models– Leverages a knowledge graph– Efficient exact inference

• Future work– Non-absolute relations– Spatial relations between object instances