Zero-Shot Learning byConvex Combination of Semantic Embeddings

Mohammad Norouzi, Tomas Mikolov, Samy Bengio, Yoram Singer, Jonathon Shlens, Andrea Frome, Greg S. Corrado, Jeffrey Dean

Image Annotation

Lion Tiger

Apple

Orange

(100,000+ classes)

Labeled Datasets

Lion Tiger

…

Training

10000

Tiger

Apple

Lion

Orange

Bear

Training

00010

Tiger

Apple

Lion

Orange

Bear

Testing

Lion? Tiger?

Generalize to Unseen Images

How to Generalize to Unseen Labels

Training:

► Lion

► Apple

► Orange

► Tiger

► Bear

Test:

► Wolf

► Cougar

► Grapefruit

“Side information”

“Represenation of new classes”

One-Shot Learning

Wolf Cougar

[Erik G. Learned-Miller et al. Learning from one example through shared densities on transforms, 2000]

[Li Fei-Fei et al. One-shot learning of object categories, 06]

Zero-Shot Learning by Supervised Attributes

•Mammal, quadruped, white / gray / black, big pointed ears, not spotted, …

•Mammal, quadruped, brown / gray, wild, long tail, not spotted, …

Wolf

Cougar

[Ali Farhadi et al. Describing objects by their attributes, 09]

[Christoph Lampert et al. Learning to detect unseen object classes by between-class attribute transfer, 09]

Zero-Shot Learning by Unsupervised Embeddings•Dog – Bear

•Cat – Tiger – Lion

•Orange – Lemon

Use embedding of labels in a vector space and a notion of semantic similarity in that space

Wolf

Cougar

Grapefruit

[Richard Socher et al. Zero-shot learning through cross-modal transfer, 13]

[Andrea Frome, Greg S. Corrado, Jon Shlens et al.DeViSE: A Deep Visual-Semantic Embedding Model, 13]

Semantic Embedding of Labels

apple

orange

tigerlion

bear

Semantic Embedding of Labels

cougar

grapefruitwolf

tiger

apple

orange

lion

bear

Semantic Embedding of Images

𝑓 (𝑥)

lion lion

Semantic Embedding of Images

tiger

𝑓 (𝑥)

tiger

Semantic Embedding of Images

cougar

► How to define the semantic embedding of labels► How to project images into that space► We use kNN search for label retrieval

𝑓 (𝑥)

Unsupervised Label Embedding

Word with similar context will get similar vectors

We apply Skip-gram to Wikipedia articles [Tomas Mikolov et al. Efficient estimation of word representations in vector space, 13] (word2vec)

Unsupervised Label Embedding

ConSE: Convex Combination of Semantic Embeddings

Tiger

Apple

Lion

Orange

Bear

ConSE: Convex Combination of Semantic Embeddings

𝑓 (𝑥 )=∑𝑖

𝑝 (𝑦 𝑖|𝑥 ¿𝑠 (𝑦 𝑖)¿kNN search with query to find relevant labels

“lion”“lion”“tiger”“tiger”) bear”“bear”) …

ConSE: Convex Combination of Semantic Embeddings

𝑓 (𝑥 )=∑𝑖

𝑝 (𝑦 𝑖|𝑥 ¿𝑠 (𝑦 𝑖)¿

ConSE(T): Convex Combination of Semantic Embeddings

A parameter to only select top predictions is among top probabilitiesWhen , then

ConSE(T): Convex Combination of Semantic Embeddings

► No extra training is needed

► For training images as long as then

► The output of is likely to stay on the manifold of labels when top predictions are close in the sematic space (similar to LLE)

Alternative Embedding Models (1)

► Train a regression model on the training set

[Richard Socher et al. Zero-shot learning through cross-modal transfer, 13]

Alternative Embedding Models (2)

► Train a ranking model on the training set so that is closer to than

Triplet ranking hinge loss

[Andrea Frome, Greg S. Corrado, Jon Shlens et al. DeViSE: A Deep Visual-Semantic Embedding Model, 13]

Our main baseline

Experiments on ImageNet

21,841 labels in total

1000 labels for training

20,842 labels for zero-shot classification

Experiments on ImageNet

manta ray

stingray

wing chair

swivel chair

[Tomas Mikolov et al. Efficient estimation of word representations in vector space, 13]

D space

softmax layer

image

label

ConvNet

[Alex Krizhevsky et al. ImageNet Classification with Deep Convolutional Neural Networks, 12]

softmax layer

image

label

ConvNet

DeViSE [NIPS 2013]

transformation

𝑓 (𝑥) ranking obj.

𝑠(𝑦 )

ConSE(T)

image

ConvNet

∑𝑖∈𝑡𝑜𝑝𝑇

𝑝 ( 𝑦 𝑖|𝑥¿ 𝑠(𝑦 𝑖)¿

𝑓 (𝑥)

softmax layer

ConSE(T)

image

ConvNet

∑𝑖∈𝑡𝑜𝑝𝑇

𝑝 ( 𝑦 𝑖|𝑥¿ 𝑠(𝑦 𝑖)¿

𝑓 (𝑥)

softmax layer

image

ConvNet

DeViSE

transformation

𝑓 (𝑥)

Experiments on ImageNet

Training Set: ImageNet 1K

1000

Experiments on ImageNet

1000

1549

2-hops: 1 or 2 tree hops away from training labels

Experiments on ImageNet

1000

7860

3-hops

Experiments on ImageNet

1000

20,842

all

Experiments on ImageNet

Training

2-hops

3-hops

Training labels candidate labels

Data Set Model #Candidate

Labels

Flat hit@k (%)

1 2 5 10 20

2-hop

DeViSE

1,549

6.0 10.0 18.1 26.4 36.4ConSE(1) 9.3 14.4 23.7 30.8 38.7

ConSE(10) 9.4 15.1 24.7 32.7 41.8

ConSE(1000) 9.2 14.8 24.1 32.1 41.1

Training labels candidate labels

Data Set Model #Candidate

Labels

Flat hit@k (%)

1 2 5 10 20

2-hop

DeViSE

1,549

6.0 10.0 18.1 26.4 36.4ConSE(1) 9.3 14.4 23.7 30.8 38.7

ConSE(10) 9.4 15.1 24.7 32.7 41.8

ConSE(1000) 9.2 14.8 24.1 32.1 41.1

Data Set Model #Candidate

Labels

Flat hit@k (%)

1 2 5 10 20

2-hop

DeViSE

1,549

6.0 10.0 18.1 26.4 36.4ConSE(1) 9.3 14.4 23.7 30.8 38.7

ConSE(10) 9.4 15.1 24.7 32.7 41.8

ConSE(1000) 9.2 14.8 24.1 32.1 41.1

Training labels candidate labels

ConSE(10) > ConSE(1) by 1% – 8%

Data Set Model #Candidate

Labels

Flat hit@k (%)

1 2 5 10 20

2-hop

DeViSE

1,549

6.0 10.0 18.1 26.4 36.4ConSE(1) 9.3 14.4 23.7 30.8 38.7

ConSE(10) 9.4 15.1 24.7 32.7 41.8

ConSE(1000) 9.2 14.8 24.1 32.1 41.1

Training labels candidate labels

ConSE(10) > ConSE(1000) by 2%

Data Set Model #Candidate

Labels

Flat hit@k (%)

1 2 5 10 20

2-hop

DeViSE

1,549

6.0 10.0 18.1 26.4 36.4ConSE(1) 9.3 14.4 23.7 30.8 38.7

ConSE(10) 9.4 15.1 24.7 32.7 41.8

ConSE(1000) 9.2 14.8 24.1 32.1 41.1

Training labels candidate labels

ConSE(10) > DeViSE by 57% – 15%

Data Set Model #Candidate

Labels

Flat hit@k (%)

1 2 5 10 20

2-hop

DeViSEConSE(1)ConSE(10)ConSE(1000)

1,549

6.09.39.49.2

10.014.415.114.8

18.123.724.724.1

26.430.832.732.1

36.438.741.841.1

3-hop

DeViSEConSE(1)ConSE(10)ConSE(1000)

7,860

1.72.62.72.6

2.94.24.44.3

5.37.37.87.6

8.210.811.511.3

12.514.816.115.7

Training labels candidate labels

Data Set Model #Candidate

Labels

Flat hit@k (%)

1 2 5 10 20

2-hop

DeViSEConSE(1)ConSE(10)ConSE(1000)

1,549

6.09.39.49.2

10.014.415.114.8

18.123.724.724.1

26.430.832.732.1

36.438.741.841.1

3-hop

DeViSEConSE(1)ConSE(10)ConSE(1000)

7,860

1.72.62.72.6

2.94.24.44.3

5.37.37.87.6

8.210.811.511.3

12.514.816.115.7

ImageNet 21K

DeViSEConSE(1)ConSE(10)ConSE(1000)

20,842

0.81.31.41.3

1.42.12.22.1

2.53.63.93.8

3.95.45.85.6

6.07.68.38.1

Training labels candidate labels

Data Set Model #Candidate

Labels

Flat hit@k (%)

1 2 5 10 20

2-hop

DeViSEConSE(1)ConSE(10)ConSE(1000)

1,549

6.09.39.49.2

10.014.415.114.8

18.123.724.724.1

26.430.832.732.1

36.438.741.841.1

Training labels candidate labels

2-hop(+1K)

DeViSEConSE(1)ConSE(10)ConSE(1000)

1,549+1000

0.80.20.30.3

2.77.16.26.2

7.917.217.016.7

14.224.024.924.5

22.731.833.532.9

Test Image ConvNet DeViSE ConSE(10)

sea lioncarpenter’s planecowboy bootloggerheadgoose

elephantturtleturtleneckflip-flopcart, handcart

California sea lionSteller sea lionAustralian sea lionSouth American sea lioneared seal

(Stellar sea lion)

Test Image ConvNet DeViSE ConSE(10)

sea lioncarpenter’s planecowboy bootloggerheadgoose

elephantturtleturtleneckflip-flopcart, handcart

California sea lionSteller sea lionAustralian sea lionSouth American sea lioneared seal

hamsterbroccoliPomeranianCapuchinweasel

golden hamsterrhesus monkeypipeshakerAmerican mink

golden hamsterrodent, gnawerEurasian hamsterrhesus monkeyrabbit, coney, cony

(Stellar sea lion)

(golden hamst.)

Test Image ConvNet DeViSE ConSE(10)

wigfur coatSalukiAfghan houndstole

water spanieltea gownbridal gownspanieltights, leotards

business suitdress, frockhairpiece, false hairswimsuitkit, outfit

Ostrichblack storkvulturecranepeacock

heronowl, bird of nighthawkraptorfinch

flightless bird, ratitePeafowlcommon spoonbillNew World vultureGreek partridge

(dress, frock)

(flightless bird)

Test Image ConvNet DeViSE ConSE(10)

threshertractorharvesterhalf-tracksnowplow

truckskiddertank carautomatic riflehouse trailer

flatcar, flatbed, flattruck, motortrucktracked vehiclebulldozer, dozerwheeled vehicle

Tibetan mastifftiti monkeyKoalallamachow-chow

kernellittoral zonecarillonCabernet Sauvignonpoodle dog

domestic dogdomestic catschnauzerBelgian sheepdogdomestic llama

(Lama pacos)

(farm machine)

Why ConSE > DeViSE zero-shot learning?

► ConSE < DeViSE on the training set

► In DeViSE the function is unrestricted, so it can project images out of the manifold of labels

► In ConSE the projection is much more restricted. We have to stay on the manifold, so the projection function is much more regularized

Conclusion

ConSE: A deterministic way to embed images in a semantic embedding space using probabilistic predictions of a classifier

Experiments suggest that this model performs very well for zero-shot learning compared to regression based algorithms

Thank you!Liger?