Semantic Image and Text Alignment: Automated Storyboard Synthesis for Digital Advertising.

Introduction

Welcome to my blog! Today, we embark on an exhilarating journey into the realm of Semantic Image and Text Alignment, a revolutionary technique poised to redefine digital advertising as we know it. Through our exploration, we'll uncover the transformative power of this innovative approach, which streamlines the synthesis of storyboards for digital ad campaigns, unlocking unparalleled efficiency and creativity in the process.

In the ever-evolving landscape of digital advertising, the seamless alignment of textual concepts with visual elements is indispensable. Semantic Image and Text Alignment represents a monumental leap forward, empowering advertisers to effortlessly translate abstract ideas into captivating visual narratives.

Recent advancements in machine learning, natural language processing, and computer vision have sparked a revolution, blurring the lines between textual concepts and visual storytelling. With the emergence of Large Language Models (LLMs), we've entered an era where the most intricate ideas can seamlessly transition into captivating visual narratives.

These transformative technologies empower us to process and interpret data with unprecedented intricacy, paving the way for the creation of dynamic content that captivates audiences like never before. By integrating machine learning, natural language processing, and computer vision, we not only simplify the translation of abstract ideas into tangible visuals but also amplify creativity and efficiency in content generation.

Image generation

The ImageGenerator class is a powerful tool that simplifies image generation and manipulation based on textual descriptions. It leverages several libraries, including Replicate, Pillow, requests, and base64, to enhance image processing capabilities and facilitate HTTP requests.

By utilizing Replicate, you can seamlessly execute any public model directly from your Python code. For instance, the code snippet below demonstrates how to run the stability-ai/stable-diffusion model:

import os
import logging
from typing import Literal,List,Tuple,Optional,Dict
import base64
from dotenv import load_dotenv
from io import BytesIO
import uuid
import requests
import replicate
from PIL import Image
import requests
from pydantic import HttpUrl
from http.client import HTTPResponse
class ImageGenerator:
def generate_image(prompt: str, performance_selection: Literal['Speed', 'Quality', 'Extreme Speed'] = "Extreme Speed", 
                       aspect_ratios_selection: str = "1024*1024", image_seed: int = 1234, sharpness: int = 2) -> Optional[dict]:

output = replicate.run(
               "stability-ai/sdxl:39ed52f2a78e934b3ba6e2a89f5b1c712de7dfea535525255b1aa35c5565e08b",
               input={
                   "prompt": prompt,
                   "performance_selection": performance_selection,
                   "aspect_ratios_selection": aspect_ratios_selection,
                   "image_seed": image_seed,
                   "sharpness": sharpness
               }
           )        

Labeling with YOLO

YOLO, known for its remarkable speed and accuracy, is a real-time object detection system that stands out in the field. Unlike traditional methods requiring multiple passes through an image, YOLO streamlines object detection by processing the entire image in a single forward pass, rendering it exceptionally efficient for our purposes.

Utilizing YOLO for Asset Detection and Localization

In our project, the integration of YOLO plays a pivotal role in automatically detecting and localizing diverse assets within images. Through meticulous training on labeled datasets containing comprehensive positional and dimensional information about assets, YOLO gains the ability to identify and delineate crucial elements such as logos, text, and interactive components.

Anchor Box Optimization

The efficacy of YOLO is further enhanced by its use of anchor boxes, which aid in accurately predicting bounding box coordinates, thereby improving localization precision. By iteratively optimizing anchor boxes during training, YOLO adapts to the unique characteristics of our advertisement dataset, ensuring optimal performance.

Streamlining the Labeling Process

To streamline the labeling process, we employed a specialized tool called YOLO Label. While effective for prominent and easily identifiable elements, we encountered challenges in distinguishing certain category labels within the images. Given resource constraints and the complexity of labeling all categories manually for every image, we initially focused on training YOLO to recognize readily identifiable elements like logos and product images.

Label an initial batch manually

We begin by manually labeling the first batch of images. In this stage, it is crucial to choose a diverse set of images to improve the trained model's generalization.?

In our specific case, we began by manually labeling 192 advertisement images, identifying and categorizing relevant elements like logos, call-to-action buttons, product images, text elements, and interactive elements.?

Train the initial model Once the first batch of images is labeled, it's time to train the initial model. This initial dataset serves as the foundation for training a YOLO model, specifically the pre-trained YOLOv5 model, to automate the labeling process for the remaining images.

Steps to train the model:

1. Create the dataset.yaml file: This file contains the path to the train and validation datasets and the list of labels used for labeling.
2. Create labels: After annotating images using Yolo Label, export the labels to YOLO format with a *.txt file per image.
3. Organize directories: Organize the train and validation images and labels into their corresponding folders. The folder structure should be as follows:../datasets/train/images/img.png../datasets/train/labels/img.txt
4. Train the model.

Future Work

Semantic understanding is crucial in machine learning, particularly for visual elements, and labeling plays a pivotal role in providing this understanding. In our context, labeling acts as the conduit through which our models grasp the significance and context of each asset within an advertisement. Supervised learning, which heavily depends on labeled data, becomes indispensable in this process. By annotating images with pertinent information about the presence and attributes of different elements, we construct a labeled dataset that serves as the bedrock for training our models. This facilitates the acquisition of patterns, correlations, and nuanced relationships among various components, empowering the models to make informed decisions during the automated composition process with precision and efficacy.

References

• https://www.claravine.com/the-ins-and-outs-of-dynamic-creative-optimization-dco/
• https://github.com/konieshadow/Fooocus-API
• https://medium.com/@genebernardin/fooocus-a-free-open-source-image-generation-tool-2a7be1b8ec1

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