Automatic generation of natural language descriptions of visual scenes
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Automatic generation of natural language descriptions of visual scenes. Prof. S. Pulman P. Blunsom N.T. Crook. Motivation. Seek to combine Computer Vision with Natural Language Processing for applications in intelligent transport systems.

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Automatic generation of natural language descriptions of visual scenes

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Automatic generation of natural language descriptions of visual scenes

Automatic generation of natural language descriptions of visual scenes

Prof. S. Pulman

P. Blunsom

N.T. Crook


Motivation

Motivation

Seek to combine Computer Vision with Natural Language Processing for applications in intelligent transport systems.

  • Driver assisted technology using in-car speech systems for

    • Hazard avoidance

      • verbal warnings of potential hazards that have been perceived by video cameras around the vehicleSystem: “A pedestrian has stepped out onto the road from the left”

    • Route planning& navigation

      • Enhancing the output of navigation devices using landmarks and other visible reference objectsSystem: “Turn left after the post-box on the corner”

      • Clarification of navigation instructionsSystem: “Take the next left”Driver: “Is that after the post-office van that is parked on the right?”

      • En route renegotiation of planned routeDriver: “I need to get some money out from a cash machine, is there one nearby?”


Application

Application

  • Driver assisted technology

“A car has pulled out onto the carriageway in front of you.”

“There is a car in the on-coming carriageway waiting to turn into the junction on the left.”

“A cyclist is crossing the carriageway ahead.”


Challenges

Challenges

  • Extract 3D relations from 2D image

  • Some relations are dependent on where the objects are in the image and whether the referent object has a direction

“The cyclist is behind the car”

“The car is behind the van”

the van is the referent object

which is facing towards the camera


Results

Results

  • Descriptions generated from images in the test set

“The Bicylist is front left of the Car”

“The Bicylist is next to the Pedestrian”

“The Pedestrian is on the Road”


Results1

Results

“The SUVPickupTruck is in front of the Car”

“The Car is behind the SUVPickupTruck”

“The Car is at the TrafficLight”


Results2

Results

“The SUVPickupTruck is in front of the Car”

“The Car is in front of the Truck_Bus”


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