Vision-Language Integration in AI:a reality check

Katerina Pastra and Yorick Wilks

Department of Computer Science, Natural Language Processing

Group,

University of Sheffield, U.K.

Setting the context

Artificial Intelligence:From technical integration of modalities multimodal meaning integrationFrom Multimedia Intellimedia + Intelligent InterfacesPurpose: intelligent, natural, coherent communication

We focus on: vision and language integration Visual modalities = images (visual perception and/or visualisation representations physically realised as e.g. 2D/3D graphics, photos…) Linguistic modalities = text and/or speech

The problem

What is computational V-L integration? (definition) How is it achieved computationally? (state of the art, practices, tendencies, needs) How far can we go? (implementation suggestions, the VLEMA prototype)

Multimodal Integration: an old AI aspiration (cf. Kirsch 1964) A wide variety of V-L integration prototypes in AI

but lack of an AI study of V-L integration, lack of a reality check

In search of a definition

Defining computational V-L Integration: could a review of related applied AI research hold the answer ?

Related work: Srihari 1994: review of V-L integration prototypes

limited number of prototypes reviewed

suggestions and implementations are mixed

no clear focus on how integration is achieved

system classification according to input type

includes cases of quasi-integration

criteria for such a review ???

The notion of quasi-integration

Quasi-integration: fusion of results obtained by modality-dependent processes (= intersection or combination of results, or even the results of one process constrain the search space for another)

The basketball player...

Our champion came first...

and the soccer player.

NLP(key phrase

identification)

IP(key frame identification

from frames that correspond to the key sentence(s) extracted)

Video summary

Defining integration through classification

It is diachronic: from SHRDLU (Winograd ´72) to conversational robots of the new millennium (e.g. Shapiro and Ismail 2003, Roy et al. 2003)

Main criterion for considering a prototype for review: V-L integration to be essential for the task the prototype is built for.

Specifics of the review:

It crosses over into diverse AI areas and applications: more than 60 prototypes reviewed from IR to Robotics

System classification criterion: the integration purpose served

Classification of V-L integration prototypes

System type Integration Process

Performance Enhancement

Mediumx analysis Mediumy analysis

(NL IU, or NLIU)

Medium Translation

Source medium analysis Target medium gen.

(image language or image language)

Multimedia Generation

Abstracted data Multimedia generation

(tabular data or knowledge representation)

Situated

Dialogue

Multimedia analysis Medium/multimedia gen.

(NL analysis and shared visual scene action/MM)

Examples

System InputIntegration Resources

Integration Mechanisms

Output

PICTIONPhotos, captions (EN)

Integr. KB Semantic Networks

Face identification

SOCCER

Soccer video, trajectories

GSD/frame, event model

Event model inst., verbalisation history...

Textual (GER) event description

MAGICPatient file Schemas Schema inst.

media selector, co-reference

Speech or text (EN), animation

CASSIESpeech (EN), 3D blocks

KL-PML asso lists

Unification Object ident. limited conversation

Beyond differences

different visual and linguistic modalities involved

different tasks performed

different integration purposes served, but

Integration resources = Associations between :

Visual and corresponding linguistic information e.g. words/concepts and visual features or image models

Form: lists, integrated KB, scene/event models in KR

Integration mechanisms = KR instantiation, translation rules, media selection, coordination…

similar integration resources are used (though represented and instantiated differently)

A descriptive definition

Descriptive Definition =

a) Intensional Definition (what the term is e.g. its genus et differentia)

b) Extensional Definition (what the term applies to)

a) Computational Vision-Language Integration is a process of associating visual and corresponding linguistic pieces of information

(indirect back-up from Cognitive Science: cf. notion of learned associations in Minsky´s "Society of Mind" 1986, and Jackendoff´s theory of associating concepts and 3D models, 1987)

b) Computational Vision-Language Integration may take the form of one of 4 integration processes according to the integration purpose to be served

The AI quest for V-L Integration

Argument :

In relying on human created data, state of the art V-L integration systems avoid core integration challenges and therefore fail to perform real integration

Simulated or manually abstracted visual input is used to avoid difficulties in image analysis Applications are restricted to blocksworlds/miniworlds scaling issues Manually constructed integration resources used to avoid difficulties in associating V-LDifficulties in integration: correspondence problem etc. but, difficulties lie there where developers intervene...

How far can we go?

Challenging current practices in V-L integration system development requires that an ambitious system specification is formulated

A prototype should:

work with real visual scenes analyse its visual data automatically associate images and language automatically

Is it feasible to develop such a prototype ???

An optimistic answer

VLEMA: A Vision-Language intEgration MechAnism

Input: automatically re-constructed static scenes in

3D (VRML format) from RESOLV (robot-surveyor) Integration task: Medium Translation

from images (3D sitting rooms) to text (what and where in EN)

Domain: estates surveillance Horizontal prototype Implemented in shell programming and ProLog

The Input

OntoVis+ KB

“…a heater … and a sofa with 3 seats…”

Description

Data Transformations

Object Segmentation

Object Naming

System Architecture

The Output

Wed Jul 7 13:22:22 GMTDT 2004

VLEMA V1.0

Katerina Pastra@University of Sheffield

Description of the automatically constructed VRML file

“development-scene.wrl”

This is a general view of a room.

We can see the front wall, the left-side wall, the floor,

A heater on the lower part of the front-wall and a sofa with 3 seats.

The heater is shorter in length than the sofa.

It is on the right of the sofa.

Conclusion

*** Could occasional reality checks re-direct (part of) AI research ? ***

Descriptive definition of V-L integration in AI

a theoretical explanatory one in:

K. Pastra (2004), “Viewing Vision-Language Integration as a Double-Grounding Case”, Proceedings of the AAAI Fall Symposium Series, Washington DC.

Review and critique of the state of the art in AI

The VLEMA prototype – a baseline for future research that will challenge current practices