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Han, Ting: Learning to Interpret and Apply Multimodal Descriptions. 2018
Inhalt
Introduction
Tasks
Thesis outline
Related work
Speech and gestures in natural communications
Typologies of hand gestures
Abstract deictics in spatial descriptions
Describe objects with iconic gestures/sketches
Relations between speech and co-verbal hand gestures
Semantic coordinations between co-verbal gestures and verbal content
Temporal alignment between gestures and speech
Multimodal human-computer interfaces
Natural language processing
Gesture recognition and interpretation
Multimodal fusion
Representation of multimodal content
Existing multimodal datasets
Summary
Multimodal corpora
Multimodal spatial scene description corpus
The scene description experiment
The spatial description experiment
Multimodal object description corpus
The Sketchy dataset
Augmenting sketches with verbal descriptions
Data statistics
The SAGA corpus
Summary
A system of understanding multimodal spatial descriptions
Modelling the interpretation of multimodal spatial descriptions
System overview
Utterance segmentation
Representing scene descriptions
Applying gestural information
Learning knowledge from prior experience
The take corpus
Learning mappings to logical forms
Learning perceptual groundings
Applying the represented knowledge
Experiment
A scene description corpus
Evaluation
Results
Summary
Towards real-time understanding of multimodal spatial descriptions
Real-time understanding of spatial scene descriptions
System overview
Gesture detection
Gesture interpretation
Utterance segmentation
Natural language understanding
Multimodal fusion & application
System evaluation
Gesture detector evaluation
Gesture interpretation evaluation
Utterance segmentation evaluation
Whole system evaluation
Incremental evaluation
Human understanding
Summary
Investigate symbolic and iconic modes in object descriptions
Draw and Tell: iconic and symbolic modes in object descriptions
Model the meaning of multimodal object descriptions
Grounding verbal descriptions
Comparing sketches with images
Fusion
Experiments
The image retrieving task
Metrics
Experiment 1: Mono-modal models
Experiment 2: multimodal models
Experiment 3: reduced sketch details
Discussion
Summary
Learning semantic categories of multimodal descriptions
Represent multimodal utterances with semantic concepts
Task formulation
Modelling the learning of multimodal semantics
Experiments
Language semantics
Gesture semantics
Multimodal semantics
Summary
Conclusion and future work
Overview of the dissertation
Future work
References