November 21, 2024
Perplexity is, historically speaking, one of the "standard" evaluation metrics for language models. And while…
With technological advancements, many multimedia data requests efficient ways to search for and obtain information across several methodologies. Cross-modal retrieval frameworks have been developed through research using AI and CV. Cross-modal retrieval is a branch of computer vision and natural language processing that links visual and verbal descriptions.
This article explores the fascinating field of cross-modal retrieval, specifically image-to-text and text-to-image search, and these tasks’ challenges, methods, and uses.
Cross-modal retrieval is the process of looking for relevant details using various techniques, including text and visuals. Finding textual labels or comments properly representing a particular image is the aim of image-to-text search. In contrast, text-to-image search looks to find relevant pictures based on a given textual query. Cross-modal retrieval techniques let us investigate and glean valuable insights from multimodal material by using the connections between visuals and text.
Building the Model
Deep learning techniques have proven to be highly effective in performing cross-modal retrieval. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are often employed to extract meaningful representations from images and text, respectively. These representations, or embeddings, capture the semantic and visual similarities between different modalities. By training a joint model that maps images and textual data into a shared embedding space, we can measure their compatibility and similarity.
In the case of image-to-text search, deep learning models such as VGG16 or ResNet can be used to extract image features. These features are then compared with text embeddings generated by processing textual descriptions using techniques like word embeddings or recurrent neural networks. The model is trained to minimize the discrepancy between the visual and textual embeddings, allowing for accurate retrieval of relevant textual descriptions given an image query.
For text-to-image search, we reverse the process. Textual queries are transformed into embeddings using methods like word embeddings or recurrent neural networks. These embeddings are matched with image features extracted from a pre-trained CNN, such as VGG16 or Inception, to identify visually relevant images. Techniques like generative models, such as generative adversarial networks (GANs), can also be employed to generate images based on textual descriptions and match them with the query text.
The following steps are involved while building the model for cross-modal retrieval.
Before you start working or running the code, ensure you have TensorFlow installed in your working environment or Colab.
Note: For the sample of the code, we have used simulated random images with the shape (224, 224, 3). These images are generated using the NumPy library’s np.random.random function creates arrays filled with random numbers between 0 and 1. The shape (224, 224, 3) corresponds to a standard RGB image size commonly used in computer vision tasks.
!pip install tensorflow --q
!pip install matplotlib --q
Next, load all the required dependencies as shown below:
from tensorflow.keras.applications.vgg16 import VGG16
from tensorflow.keras.applications.vgg16 import preprocess_input
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input, Dense, Embedding, LSTM, concatenate
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
import numpy as np
import matplotlib.pyplot as plt
Generate simulated data for images, texts, and labels. Create a NumPy array, images
, holding randomized image data. Formulate a list, texts
, containing the same text for all samples. Construct a NumPy array, labels
, populated with random binary labels.
num_samples = 100
image_shape = (224, 224, 3)
max_length = 20
vocab_size = 10000
embedding_dim = 100
num_classes = 2
images = np.random.random((num_samples, *image_shape))
texts = ['I like eating Bananas'] * num_samples
labels = np.random.randint(2, size=(num_samples, num_classes))
Take the crucial step of preprocessing the images using the preprocess_input
function. This function is pivotal in preparing images for various neural network architectures. Employ the Tokenizer
class to tokenize and index the words present in the texts
. Transform these indexed text sequences into text_sequences
using texts_to_sequences
. Complete this process by ensuring uniformity in sequence length using pad_sequences
.
images_preprocessed = np.array([preprocess_input(img) for img in images])
tokenizer = Tokenizer(num_words=vocab_size)
tokenizer.fit_on_texts(texts)
text_sequences = tokenizer.texts_to_sequences(texts)
text_sequences_padded = pad_sequences(text_sequences, maxlen=max_length)
Construct an image input tensor via the Input
class. Load a pre-trained VGG16 model and extract the final fully connected layer (‘fc2’) responsible for feature extraction. The extracted features are contained in vgg_output
.
Then, initiate the formation of a text input tensor. Leverage an embedding layer to convert tokenized text sequences into dense vectors. Subsequently, process these embeddings with an LSTM layer to capture sequential nuances within the text.
image_input = Input(shape=image_shape)
vgg_model = VGG16(weights='imagenet', include_top=True)
vgg_model = Model(inputs=vgg_model.input, outputs=vgg_model.get_layer('fc2').output)
vgg_output = vgg_model(image_input)
text_input = Input(shape=(max_length,))
embedding_layer = Embedding(vocab_size, embedding_dim, input_length=max_length)(text_input)
lstm_layer = LSTM(256)(embedding_layer)
Unify the outputs of the VGG and LSTM models through concatenation, followed by the RElu activation function. Craft the output layer, characterized by a dense configuration housing num_classes
neurons and employing a softmax activation function. This setup enables the prediction of class probabilities. Then, compile the model, harnessing the power of the Adam optimizer and categorical cross-entropy loss. The accuracy metric is also implemented to gauge performance.
combined = concatenate([vgg_output, lstm_layer])
dense1 = Dense(256, activation='relu')(combined)
output = Dense(num_classes, activation='softmax')(dense1)
cross_modal_model = Model(inputs=[image_input, text_input], outputs=output)
cross_modal_model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
Dive into the training phase, where the model receives preprocessed image and text data and corresponding labels. The training unfolds over a single epoch, allowing the model to gain initial insights. Next, prepare the query image for analysis by subjecting it to the same preprocessing steps used on the training images.
cross_modal_model.fit([images_preprocessed, text_sequences_padded], labels, epochs=1, batch_size=32)
query_image = images[0][np.newaxis, ...]
query_text = 'I like eating Bananas'
query_image_preprocessed = preprocess_input(query_image)
image_features = vgg_model.predict(query_image_preprocessed)
query_sequence = tokenizer.texts_to_sequences([query_text])
query_sequence_padded = pad_sequences(query_sequence, maxlen=max_length)
text_results = ["Retrieved text 1", "Retrieved text 2"]
image_results = [images[1], images[2]]
Display the output of the model building for confirmation.
print("Image-to-Text Results:")
for result in text_results:
print(result)
print("Text-to-Image Results:")
for result in image_results:
print(result)
Following these processes, we can develop our cross-modal retrieval model—access to the full code here.
Addressing these challenges is crucial for advancing the field of cross-modal retrieval and unlocking its full potential in various applications. Researchers continue exploring innovative techniques and methodologies to overcome these challenges and improve cross-modal retrieval systems’ accuracy, efficiency, and scalability.
By incorporating these advancements into cross-modal retrieval systems, researchers aim to improve retrieval accuracy, enhance the understanding of multimodal data, and overcome challenges associated with modalities mismatch and limited labeled data. These techniques provide promising directions for future research and development in cross-modal retrieval.
Cross-modal retrieval, especially image-to-text and text-to-image search, brings up fascinating possibilities to explore and analyze multimodal data. We can use deep learning approaches to create models that comprehend and extract pertinent information from several modalities. We may anticipate increasingly accurate, efficient, and adaptable cross-modal retrieval methods as the discipline develops, allowing us to extract essential insights from the immense sea of multimedia data.