background

Recently, I find a good cources about multimodal machine learning. In this blog, I will study it and note my understanding.

O

master multimodal basic work

KR

1. what is modality ?
2. multimodal develop history
3. main area in multimodal

1.what is modality ?

modality :

• the way in which something happens or experienced.
• it includes sensory form(touch,feel) or a certain type of information(image, speech).

Medium :

• a means for storing or communicating information.
  
  Here is examples of modalities:
  

2. multimodal develop history

1. The “behavioral” era (1970s until late 1980s)
   The McGurk Effect (1976)
   

2. The “computational” era (late 1980s until 2000)
   Audio-Visual Speech Recognition (AVSR)
   Affective Computing

3. The “interaction” era (2000 - 2010)
   Human Multimodal Interaction ways.
   

4. The “deep learning” era (2010s until …)
   

3. main areas in multimodal

multimodal has 5 core thories, 37 applicationes, 235 related work.
here are five areas.

1. Representation

Definition : Learning how to represent and summarize multimodal data in away
that exploits the complementarity and redundancy.

demo :

main framewrok :

coordinated representaions is aiming to max corrlelated and make uncorrelated ventors distincitly.

2. Alignment

find correspondences between elements of modalities.

Demo :

3. fusion

Definition: To join information from two or more modalities to perform a
prediction task.

1. it is not talking about detail model name,But fcou on when, how, what to fusion.
   
2. Model-Based (Intermediate) Approaches

1. Deep neural networks
2. Kernel-based methods
3. Graphical models

4. Translation

Definition: Process of changing data from one modality to another, where the
translation relationship can often be open-ended or subjective.

5. Co-Learning

Definition: Transfer knowledge between modalities, including their
representations and predictive models.

I will omit due I am not research it.

5. summary