Beginner Explanation
Imagine you have a friend who can understand both spoken words and pictures. If you tell them a story and show them a drawing related to it, they can connect the two and understand the whole idea better. Multimodal models work the same way! They are like super-smart friends that can read text and look at images at the same time, helping them understand and make sense of information that comes in different forms.Technical Explanation
Multimodal models (LMMs) are designed to process and integrate information from various modalities, such as text, images, and audio. These models often utilize deep learning architectures that combine convolutional neural networks (CNNs) for image processing and transformer models for text processing. For example, a simple multimodal model can be built using TensorFlow and Keras: “`python from tensorflow.keras.layers import Input, Dense, Concatenate from tensorflow.keras.models import Model # Text input text_input = Input(shape=(text_length,)) # Assuming text_length is defined text_output = Dense(128, activation=’relu’)(text_input) # Image input image_input = Input(shape=(image_height, image_width, channels)) # Define image dimensions image_output = Dense(128, activation=’relu’)(image_input) # Combine outputs combined = Concatenate()([text_output, image_output]) final_output = Dense(num_classes, activation=’softmax’)(combined) # Model model = Model(inputs=[text_input, image_input], outputs=final_output) model.compile(optimizer=’adam’, loss=’categorical_crossentropy’, metrics=[‘accuracy’]) “` This model takes both text and image inputs, processes them separately, and then combines the results for a unified output.Academic Context
Multimodal models have gained significant attention in the AI/ML community due to their ability to process heterogeneous data. Key research areas include representation learning, where models learn joint embeddings for different modalities, and attention mechanisms that allow them to focus on relevant parts of the input. Notable papers include ‘Learning Transferable Visual Models From Natural Language Supervision’ by Radford et al., which introduces CLIP, and ‘ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision’ by Kim et al., which explores transformer architectures for multimodal tasks. The mathematical foundation often involves tensor operations and attention mechanisms, allowing for effective integration of diverse data types.Code Examples
Example 1:
from tensorflow.keras.layers import Input, Dense, Concatenate
from tensorflow.keras.models import Model
# Text input
text_input = Input(shape=(text_length,)) # Assuming text_length is defined
text_output = Dense(128, activation='relu')(text_input)
# Image input
image_input = Input(shape=(image_height, image_width, channels)) # Define image dimensions
image_output = Dense(128, activation='relu')(image_input)
# Combine outputs
combined = Concatenate()([text_output, image_output])
final_output = Dense(num_classes, activation='softmax')(combined)
# Model
model = Model(inputs=[text_input, image_input], outputs=final_output)
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])Example 2:
from tensorflow.keras.layers import Input, Dense, Concatenate
from tensorflow.keras.models import Model
# Text input
text_input = Input(shape=(text_length,)) # Assuming text_length is definedExample 3:
from tensorflow.keras.models import Model
# Text input
text_input = Input(shape=(text_length,)) # Assuming text_length is defined
text_output = Dense(128, activation='relu')(text_input)View Source: https://arxiv.org/abs/2511.16672v1