How can i make vision transformer model that recives input, multiple images

Is it possible to create or learn a deep learning model in Matlab that receives multiple images as input and has one sequence as output?

For example, I wonder how to receive 20 consecutive images as inputs and output a sequence such as '11153'.

Thanks for reading

Answer 1

[Debraj Maji]

Hi @수민 안

I understand that you are trying to create a Vision Transformer(ViT) Model which takes multiple images as input and generates a sequence.

Creating a Vision Transformer (ViT) model that receives multiple images as input in MATLAB involves adapting the ViT architecture to handle sequences of images. The original ViT architecture is designed for single-image classification tasks. To modify it for sequential multi-image input, you would treat each image as a token in the sequence and process these tokens in a way similar to how transformers process sequential data in natural language processing (NLP).

Here is a conceptual outline of how you could approach this:

Step 1: Preprocessing:

• Resize all images to a fixed size.
• Flatten each image into a 1D vector or use patches as tokens, as done in ViT.
• Optionally, add positional encoding to retain the order of the images.

Step 2: Transformer Encoder:

• Use a series of transformer encoder layers to process the sequence of image tokens.
• Each transformer encoder layer would include multi-head self-attention and feedforward neural networks.

Step 3: Sequence Decoder:

• After processing the images through the transformer encoder, you need to decode the output into a sequence.
• You can use an RNN, LSTM, or another transformer decoder to generate the output sequence.

Step 4: Output Layer:

• The output layer would produce the final sequence, which could be a series of classification layers, one for each position in the output sequence.

In MATLAB, you can use Deep Learning Toolbox to create custom layers and models. Currently MATLAB does support a pre-defined ViT. However this scenario would require you to implement the transformer layers manually. You can follow this documentation for steps on how to define custom Deep Learning Layers:

https://www.mathworks.com/help/deeplearning/ug/define-custom-deep-learning-layer.html

For the Pretrained ViT available in MATLAB you can refer to the following documentation: https://www.mathworks.com/help/vision/ref/visiontransformer.html

For additional info on pre-defined Deep Learning Layers in MATLAB you can refer to the following link:

https://www.mathworks.com/help/deeplearning/ug/list-of-deep-learning-layers.html

I hope this resolves your query.

With regards,

Debraj.

Answer 2

[Shubham]

Hi 수민 안,

Yes, it is possible to create a deep learning model in MATLAB that takes multiple images as input and outputs a sequence of numbers. This can be done by using a Convolutional Neural Network (CNN) for image feature extraction combined with a Recurrent Neural Network (RNN) or Long Short-Term Memory (LSTM) network for sequence prediction.

Here's a high-level overview of how you might approach this:

1. Data Preparation:

• Organize your images and corresponding sequence labels.
• Preprocess the images (resizing, normalization, etc.).
• Split the data into training, validation, and test sets.

2. Model Architecture:

• Use a CNN as the feature extractor for the images. You can use pre-trained networks like VGG, ResNet, or create your own.
• Flatten the output of the CNN or use global pooling to reduce the dimensionality.
• Feed the output into an RNN or LSTM layer(s) to handle the sequence prediction.
• The final output layer should have the number of units corresponding to the length of your output sequence with a softmax activation if you are treating each position as a classification problem.

3. Training:

• Compile the model with an appropriate loss function (e.g., categorical cross-entropy if you're treating the sequence prediction as a classification problem).
• Train the model using the training data with validation data to monitor performance.

4. Evaluation and Testing:

• Evaluate the model's performance on the test set.
• Adjust hyperparameters or model architecture as needed based on performance.

5. Prediction:

• Use the trained model to predict sequences from new sets of images.

I hope this helps!