Research Paper

Chain Fusion: a novel fusion method in multimodal learning

https://drive.google.com/file/d/1zc3QwAuZ8OuWiNheckDy-ldBtlTjmEny/view?usp=sharing

Poster

Research in Multimodal Machine Learning has been growing in importance and interest, because it can provide major advantages and results, when compared to unimodal approaches. Methods in this area have reached fields like multimedia classification, audiovisual automatic speech recognition, generation of natural language descriptions of videos, among other applications.

Project Description: Grupo: T02-09

Initial:

Follow the discusion in classes we decide to propose a new form to make fusion.
Definition of fusion

Notes

Descrever multimodal Descrever várias fase do multimodal (resumido) Descrever a fase de foco (fusion) -Existe early, late and deep fusion Queremos propor uma nova forma de fazer Fusion (Chain Fusion) Dizer que a nossa proposta é inspirada nos Chain classifiers

Correr os bencharmks originais Temos late fusion com tabular e image - benchmark Temos chain fusion benchmark (coorre o vgg normalmente e juntas numa rede) ->Late fusion com tres: imagem & tabular & texto(NPL) ->Late fusion tabular & imagem
->Late fusion imagem NPL -> https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8269806&fbclid=IwAR1C_TKJXvLIdOsgkOiwX_A10pZocHEiBOvhgwjeYgWiTf9B7_N3PiszMQM&tag=1

A imagem entra num cnn e vai para outra red

->treinar image.py que entra numa rede . ->treinar o tabular.py e retirar as features -> Juntar os dois

late fusion: treinar image.py uma cnn -vgg16 treinar o tabular.py - smodelo sequencial implementado os dois inputs numa camada superior que junta as duas informações

Chain fusion: treinar image.py uma cnn -vgg16 Recebe o ultimo input + o tabular.py (nao treinado ainda/sem pesos)

OverLeaf Paper Link:

https://www.overleaf.com/1224357339fvmfywcnsrqp

Index

1. Bags_of_words.py -> gera o vocab.txt
2. data_preraration.py -> gera o prepared_data.csv
3. text_model.py -> no csv original esta a usar a coluna product display name
4. image.py - classificacao de imagens (usa as imagens)
5. tabular.py - usa os dados que estao no balanced_data.csv
6. chain_fusion.py - esta neste momento a fazer fusion com o tabular.py e o image.py

Used datase:

https://www.kaggle.com/paramaggarwal/fashion-product-images-small 

Note: (copy 'styles.csv' and 'images' folder into 'data' folder)

Tutorial:

https://www.kaggle.com/paramaggarwal/fashion-product-images-classifier

Run

$ pip3 install -r requirements.txt
$ python3 chain_fusion.py

EXAMPLE: https://github.com/drscotthawley/audio-classifier-keras-cnn

Main Paper

Improving LSTM-based Video Description with Linguistic Knowledge Mined from Text LINK: https://arxiv.org/abs/1604.01729

Selected Papers

• Multimodal Machine Learning: A Survey and Taxonomy

   -Instead of focusing on specific multimodal applications, this paper surveys the recent advances in multimodal machine learning itself and presents them in a common taxonomy. We go beyond the typical early and late fusion categorization and identify broader challenges that are faced by multimodal machine learning, namely: representation, translation, alignment, fusion, and co-learning.
  

• Sense adaptive multimodal information fusion: A proposed model

   -Describes three broad categories of multimodal fusion extensively used viz. early, late and transmedia fusion.
   keyword: multimodal fusion
  

Notes

1. The input ~x to the first LSTM layer is a sequence of frame features obtained from the penultimate layer (fc 7 ) of a Convolutional Neural Network (CNN) after the ReLu operation. This LSTM layer encodes the video sequence. After viewing all the frames, the second LSTM layer learns to decode this state into a sequence of words

2. Early Fusion. Our first approach (early fusion), is to pre-train portions of the network modeling language on large corpora of raw NL text and then continue “fine-tuning” the parameters on the paired video-text corpus

3. Late Fusion. Our late fusion approach is similar to how neural machine translation models incorporate a trained language model during decoding. At each step of sentence generation, the video caption model proposes a distribution over the vocabulary.

4. Deep Fusion. In the deep fusion approach (Fig. 2), we integrate the LM a step deeper in the generation process by concatenating the hidden state of the language model LSTM (h LM) with the hidden state t of the S2VT video description model (h V t M ) and use the combined latent vector to predict the output word.

5. Evaluation Metrics: We evaluate performance using machine translation (MT) metrics to compare the machine generated descriptions to human ones

Other Papers related

This section contains a list of related papers.

• Audio Set: An ontology and human-labeled dataset for audio events

• Robust Sound Event Classification Using Deep Neural Networks

• Hierarchical classification of audio data for archiving and retrieving

• Audio Classification Method Based on Machine Learning

• AUDIO FEATURE EXTRACTION AND ANALYSIS FORSCENE SEGMENTATION AND CLASSIFICATION

• Convolutional recurrent neural networks for music classification

Speech Recognition (FESR):

Paper:

Recent Advances in the Automatic Recognition of Audio-Visual Speech

Repos:

Biblioteca de pyhon Audioanalysis:

https://github.com/xiao2mo/pyAudioAnalysis

https://github.com/gionanide/Speech_Signal_Processing_and_Classification (using feature extraction code from this one)

https://github.com/Angeluz-07/audio-processing-files (audio mp3 files with extracted dataset examples)

https://github.com/Angeluz-07/audio-processing-data (results from feature extraction)

https://www.kaggle.com/ashishpatel26/feature-extraction-from-audio (using for test sets)

https://musicinformationretrieval.com/mfcc.html (Librosa conversion)

DataSets

1. http://research.google.com/audioset/dataset/index.html

Contains classification of the audio

Presentation ideas

The goals description is determined by crowdsourced human evaluations;

We are going to approach the problem as a classification of audiovisual speech instead of a golden standard problem as we lack a proper dataset for golden standard audiovisual recognition.

In practice, using an ensemble of networks trained slightly differently can improve performance (Early fusion, Late Fusion, Deep Fusion)

We choose Late Fusion approach

How to run

1. Download AudioSet dataset
   • Each folder represents a class
   • Command: python3 download_audioset.py
2. Preprocess data
   • ATM not working with AudioSet, but rather with the Samples folder
   • Command: python3 preprocess_data.py
3. Train Network (Audio only)
   • Command: python3 train_network.py