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Sreyasi Nag Chowdhury, Niket Tandon, Gerhard Weikum Max Planck Institute for Informatics, Saarbrücken, Germany

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Sreyasi Nag Chowdhury, Niket Tandon, Gerhard Weikum

Max Planck Institute for Informatics, Saarbrücken, Germany

17/06/2016 1Sreyasi Nag Chowdhury, AKBC 2016

User Query

Concrete Abstract

Q: bicycle in street Q: environment friendly traffic

17/06/2016 1Sreyasi Nag Chowdhury, AKBC 2016

User Query

Concrete Abstract

“Wow! Double-decker buses still run!”

Q: bicycle in street Q: environment friendly traffic

17/06/2016 1Sreyasi Nag Chowdhury, AKBC 2016

User Query

Concrete Abstract

“Wow! Double-decker buses still run!”

Text-only

Q: bicycle in street Q: environment friendly traffic

17/06/2016 1Sreyasi Nag Chowdhury, AKBC 2016

User Query

Concrete Abstract

“Wow! Double-decker buses still run!”

Visual objects: bicycle, bus, car

Text-only

Text + visual

Q: bicycle in street Q: environment friendly traffic

17/06/2016 1Sreyasi Nag Chowdhury, AKBC 2016

User Query

Concrete Abstract

“Wow! Double-decker buses still run!”

Visual objects: bicycle, bus, car

Text-only

Text + visual

Q: bicycle in street Q: environment friendly traffic

“Biking by the river”

Visual objects: train, piano

17/06/2016 1Sreyasi Nag Chowdhury, AKBC 2016

User Query

Concrete Abstract

“Wow! Double-decker buses still run!”

Visual objects: bicycle, bus, car

Text-only

Text + visual

Q: bicycle in street Q: environment friendly traffic

“Biking by the river”

Visual objects: train, piano

Text-only

Text + visual

17/06/2016 1Sreyasi Nag Chowdhury, AKBC 2016

User Query

Concrete Abstract

“Wow! Double-decker buses still run!”

Visual objects: bicycle, bus, car

Text-only

Text + visual

“Riding for a cause.”

Visual objects: person, bicycle

CSK: (riding bicycle, be, environment friendly)

Q: bicycle in street Q: environment friendly traffic

“Biking by the river”

Visual objects: train, piano

Text-only

Text + visual

17/06/2016 1Sreyasi Nag Chowdhury, AKBC 2016

User Query

Concrete Abstract

“Wow! Double-decker buses still run!”

Visual objects: bicycle, bus, car

Text-only

Text + visual

“Riding for a cause.”

Visual objects: person, bicycle

Text/visual

Q: bicycle in street Q: environment friendly traffic

“Biking by the river”

Visual objects: train, piano

Text-only

Text + visual

17/06/2016 1Sreyasi Nag Chowdhury, AKBC 2016

User Query

Concrete Abstract

“Wow! Double-decker buses still run!”

Visual objects: bicycle, bus, car

Text-only

Text + visual

“Riding for a cause.”

Visual objects: person, bicycle

CSK: (riding bicycle, be, environment friendly)

Text/visual

Text + visual + CSK

Q: bicycle in street Q: environment friendly traffic

“Biking by the river”

Visual objects: train, piano

Text-only

Text + visual

17/06/2016 1Sreyasi Nag Chowdhury, AKBC 2016

User Query

Concrete Abstract

“Wow! Double-decker buses still run!”

Visual objects: bicycle, bus, car

Text-only

Text + visual

“Riding for a cause.”

Visual objects: person, bicycle

CSK: (riding bicycle, be, environment friendly)

Text/visual

Text + visual + CSK

Q: bicycle in street Q: environment friendly traffic

“Biking by the river”

Visual objects: train, piano

Text-only

Text + visual

Our contribution

CSK: Where do we get it from?

CSK: How do we use it?

CSK: How to combine noisy signals?

CSK: Does it help?

17/06/2016Sreyasi Nag Chowdhury, AKBC 2016 2

Existing CSK knowledge bases: WordNet, ConceptNet, WebChild, Knowlywood

CSK: WHERE DO WE GET IT FROM?

317/06/2016Sreyasi Nag Chowdhury, AKBC 2016

Existing CSK knowledge bases: WordNet, ConceptNet, WebChild, Knowlywood

Our corpus: Wiki articles from domain ‘tourism’

CSK: WHERE DO WE GET IT FROM?

317/06/2016Sreyasi Nag Chowdhury, AKBC 2016

Existing CSK knowledge bases: WordNet, ConceptNet, WebChild, Knowlywood

Our corpus: Wiki articles from domain ‘tourism’

Pruned by Jaccard Similarity

CSK: WHERE DO WE GET IT FROM?

317/06/2016Sreyasi Nag Chowdhury, AKBC 2016

Existing CSK knowledge bases: WordNet, ConceptNet, WebChild, Knowlywood

Our corpus: Wiki articles from domain ‘tourism’

Pruned by Jaccard Similarity

~22,000 CSK triples

“tourism” “be travel for” “recreation, leisure, family, business purposes”

“people” “fall in” “love”

---------------------------------------------------------------------------------------------------

“the bloody hell” “be” “you”

CSK: WHERE DO WE GET IT FROM?

3

Domain-specific

ReVerb triples

17/06/2016Sreyasi Nag Chowdhury, AKBC 2016

CSK: HOW DO WE USE IT?

Query string: travel with backpack

CSK to expand query

• t1: (tourists, use, travel maps)

• t2: (tourists, carry, backpack)

• t3: (backpack, is a type of, bag)

417/06/2016Sreyasi Nag Chowdhury, AKBC 2016

Query string: travel with backpack

CSK to expand query

• t1: (tourists, use, travel maps)

• t2: (tourists, carry, backpack)

• t3: (backpack, is a type of, bag)

Document x with features

• Textual: “A tourist reading a map by the road”

• Visual: person, bag, bottle, bus

Text-only systems

Text + visual + CSK systems

417/06/2016Sreyasi Nag Chowdhury, AKBC 2016

CSK: HOW DO WE USE IT?

Query string: travel with backpack

CSK to expand query

• t1: (tourists, use, travel maps)

• t2: (tourists, carry, backpack)

• t3: (backpack, is a type of, bag)

Document x with features

• Textual: “A tourist reading a map by the road”

• Visual: person, bag, bottle, bus

Text-only systems

Text + visual + CSK systems

4

CSK bridge vocabulary gap

between query and document

CSK establish relations

between concepts

CSK diminish noise from

modalities – ensemble effect

17/06/2016Sreyasi Nag Chowdhury, AKBC 2016

CSK: HOW DO WE USE IT?

5

A tour group is standing on the grass with ruins in the background.

Group of people standing in front of a stone structure.

Document x

17/06/2016Sreyasi Nag Chowdhury, AKBC 2016

CSK: HOW DO WE USE IT?

5

Document x

Textual features xx

17/06/2016Sreyasi Nag Chowdhury, AKBC 2016

CSK: HOW DO WE USE IT?

A tour group is standing on the grass with ruins in the background.

Group of people standing in front of a stone structure.

5

Document x

Textual features xx

17/06/2016Sreyasi Nag Chowdhury, AKBC 2016

CSK: HOW DO WE USE IT?

A tour group is standing on the grass with ruins in the background.

Group of people standing in front of a stone structure.

Visual features xv : backpack, person

5

Document x

Textual features xx

17/06/2016Sreyasi Nag Chowdhury, AKBC 2016

CSK: HOW DO WE USE IT?

A tour group is standing on the grass with ruins in the background.

Group of people standing in front of a stone structure.

Visual features xv : backpack, bag, container

person, casual agent, organism

5

Document x

Textual features xx

17/06/2016Sreyasi Nag Chowdhury, AKBC 2016

CSK: HOW DO WE USE IT?

A tour group is standing on the grass with ruins in the background.

Group of people standing in front of a stone structure.

Visual features xv : backpack, bag, container

person, casual agent, organism

Query: “group excursion”

Query expansion: (an excursion, be trip by, a group of people)

(organized excursions, book through, a tour company)

CSK features

A tour group is standing on the grass with ruins in the background.

Group of people standing in front of a stone structure.

517/06/2016Sreyasi Nag Chowdhury, AKBC 2016

CSK: HOW DO WE USE IT?

Visual features xv : backpack, bag, container

person, casual agent, organism

Query: “group excursion”

Query expansion: (an excursion, be trip by, a group of people)

(organized excursions, book through, a tour company)

CSK: HOW TO COMBINE NOISY SIGNALS?

6

Mixture LM:

Commonsense-aware LM:

Smoothed LM: , where

Basic LM: , where

17/06/2016Sreyasi Nag Chowdhury, AKBC 2016

CSK: HOW TO COMBINE NOISY SIGNALS?

6

Mixture LM:

Commonsense-aware LM:

Smoothed LM: , where

Basic LM: , where

17/06/2016Sreyasi Nag Chowdhury, AKBC 2016

6

Mixture LM:

Commonsense-aware LM:

Smoothed LM: , where

Basic LM: , where

17/06/2016Sreyasi Nag Chowdhury, AKBC 2016

CSK triple

CSK: HOW TO COMBINE NOISY SIGNALS?

6

Mixture LM:

Commonsense-aware LM:

Smoothed LM: , where

Basic LM: , where

17/06/2016Sreyasi Nag Chowdhury, AKBC 2016

Probabilities based on

word-wise overlaps

CSK: HOW TO COMBINE NOISY SIGNALS?

6

Mixture LM:

Commonsense-aware LM:

Smoothed LM: , where

Basic LM: , where

17/06/2016Sreyasi Nag Chowdhury, AKBC 2016

Background corpus –

Co-occurring Flickr tags

CSK: HOW TO COMBINE NOISY SIGNALS?

6

Mixture LM:

Commonsense-aware LM:

Smoothed LM: , where

Basic LM: , where

17/06/2016Sreyasi Nag Chowdhury, AKBC 2016

Textual and visual features

CSK: HOW TO COMBINE NOISY SIGNALS?

717/06/2016Sreyasi Nag Chowdhury, AKBC 2016

CSK: DOES IT HELP?

Image Dataset

o Flickr30k

o MS COCO captioned dataset

o Pascal Sentence Dataset

o SBU captioned dataset

717/06/2016Sreyasi Nag Chowdhury, AKBC 2016

CSK: DOES IT HELP?

Image Dataset

o Flickr30k

o MS COCO captioned dataset

o Pascal Sentence Dataset

o SBU captioned dataset

Boat trip to see the mythical pink dolphins...

this is John checking in with the office for that day.

717/06/2016Sreyasi Nag Chowdhury, AKBC 2016

CSK: DOES IT HELP?

Image Dataset

o Flickr30k

o MS COCO captioned dataset

o Pascal Sentence Dataset

o SBU captioned dataset

A group of tourists is crossing a bridge that connects a walking path to a trail of nature.

Many people cross a very tall footbridge with a tree-covered hill in the background.

This shows a group of people walking over an arched red bridge.

People cross a large bridge to get over the body of water.

People walking over a white and red bridge over a pond.

Boat trip to see the mythical pink dolphins...

this is John checking in with the office for that day.

717/06/2016Sreyasi Nag Chowdhury, AKBC 2016

CSK: DOES IT HELP?

Image Dataset

o Flickr30k

o MS COCO captioned dataset

o Pascal Sentence Dataset

o SBU captioned dataset

A group of tourists is crossing a bridge that connects a walking path to a trail of nature.

Many people cross a very tall footbridge with a tree-covered hill in the background.

This shows a group of people walking over an arched red bridge.

People cross a large bridge to get over the body of water.

People walking over a white and red bridge over a pond.

Boat trip to see the mythical pink dolphins...

this is John checking in with the office for that day.

social media post

blog post

717/06/2016Sreyasi Nag Chowdhury, AKBC 2016

CSK: DOES IT HELP?

Image Dataset

o Flickr30k

o MS COCO captioned dataset

o Pascal Sentence Dataset

o SBU captioned dataset

~ 50,000 images with captions

A group of tourists is crossing a bridge that connects a walking path to a trail of nature.

Many people cross a very tall footbridge with a tree-covered hill in the background.

This shows a group of people walking over an arched red bridge.

People cross a large bridge to get over the body of water.

People walking over a white and red bridge over a pond.

Boat trip to see the mythical pink dolphins...

this is John checking in with the office for that day.

social media post

blog post

Baselines: Text-only and Text + Visual search approaches

Evaluation metric: Average Precision @ 10

817/06/2016Sreyasi Nag Chowdhury, AKBC 2016

CSK: DOES IT HELP?

Baselines: Text-only and Text + Visual search approaches

Evaluation metric: Average Precision @ 10

817/06/2016Sreyasi Nag Chowdhury, AKBC 2016

CSK: DOES IT HELP?

47% 64% 85%

Text-only Text + Visual Text + Visual + CSK (Know2Look)

Baselines: Text-only and Text + Visual search approaches

Evaluation metric: Average Precision @ 10

Examples queries:

o Concrete – ball park, bridge road, table home, bicycle road

o Abstract – diesel transport, housing town

o Mixed – old clock, backpack travel, boat tour

817/06/2016Sreyasi Nag Chowdhury, AKBC 2016

CSK: DOES IT HELP?

47% 64% 85%

Text-only Text + Visual Text + Visual + CSK (Know2Look)

Baselines: Text-only and Text + Visual search approaches

Evaluation metric: Average Precision @ 10

Examples queries:

o Concrete – ball park, bridge road, table home, bicycle road

o Abstract – diesel transport, housing town

o Mixed – old clock, backpack travel, boat tour

817/06/2016Sreyasi Nag Chowdhury, AKBC 2016

CSK: DOES IT HELP?

47% 64% 85%

Text-only Text + Visual Text + Visual + CSK (Know2Look)

Co-occurring Flickr tags

917/06/2016Sreyasi Nag Chowdhury, AKBC 2016

Text-only Text + Visual Text + Visual + CSK (Know2Look)

Query: “group excursion”

xxj: “A small excursion boat anchored

on the beach at the resort in Mexico. "

xvj: lunar excursion module, conveyance xxj: “A group of people riding camels.“

yk: (an excursion, be trip by,

a group of people)

CSK: DOES IT HELP?

917/06/2016Sreyasi Nag Chowdhury, AKBC 2016

Text-only Text + Visual Text + Visual + CSK (Know2Look)

Query: “group excursion”

xxj: “A small excursion boat anchored

on the beach at the resort in Mexico. "

xvj: lunar excursion module, conveyance xxj: “A group of people riding camels.“

yk: (an excursion, be trip by,

a group of people)

CSK: DOES IT HELP?

Noisy OpenIE triples capture commonsense knowledge

Noisy textual cues + noisy visual object detection + noisy commonsense knowledge ensemble effect better results for multimodal document retrieval

CSK act as bridge between text and vision

1017/06/2016Sreyasi Nag Chowdhury, AKBC 2016

Noisy OpenIE triples capture commonsense knowledge

Noisy textual cues + noisy visual object detection + noisy commonsense knowledge ensemble effect better results for multimodal document retrieval

CSK act as bridge between text and vision

Do word co-occurrences or word embeddings provide similar results?

Does structured commonsense knowledge improve retrieval?

1017/06/2016Sreyasi Nag Chowdhury, AKBC 2016

Noisy OpenIE triples capture commonsense knowledge

Noisy textual cues + noisy visual object detection + noisy commonsense knowledge ensemble effect better results for multimodal document retrieval

CSK act as bridge between text and vision

Do word co-occurrences or word embeddings provide similar results?

Does structured commonsense knowledge improve retrieval?

1017/06/2016Sreyasi Nag Chowdhury, AKBC 2016

Thank you