Computer vision (CV) is revolutionizing merchandising and product development within the fashion industry. From assisting individuals in creating a personalized brand, monitoring fabric quality, and augmenting designers’ creativity to optimizing marketing strategies and ensuring the perfect fit, AI can be instrumental across the entire value chain of the fashion industry.
Heading the computer vision and machine vision team, I’ve had the privilege of working with our cutting-edge accelerator, IVA, for several years. Our primary goal is to delve into how computer vision, particularly through IVA, can benefit various operations within the fashion industry.
Let’s begin our journey of optimizing the fashion industry at the first step in creating a new garment: Understanding and predicting what’s selling.
Trend Analysis and Demand Forecasting
Designing serves as the critical bridge linking fashion, manufacturing, and marketing. However, a strategic design must be created before manufacturing a piece of clothing, considering aesthetics and timely production. This challenge is multifaceted, as designers must predict trends, liaise with marketing teams, and ensure the products align with expected demand, thereby reducing waste and unsold stock.
Mining data from sources such as social media and films helps to anticipate trends. There are two primary avenues where the blend of computer vision (CV), natural language processing (NLP), and Generative AI (GenAI) used in IVA shines: Detection/Recognition and Synthesis.
Detection/Recognition/Caption: Unlocking Brand Uniqueness
IVA operates on three levels, leveraging CV and NLP. Firstly, detection, where specific elements within an image are identified, like discerning a person’s attire and accessories. Secondly, recognition provides a label for the detected items, offering a deeper context. Lastly, captioning provides a descriptive tagline, like identifying someone as wearing “a white shirt with a red collar.”
This powerful application lets us detect potential brand imitations online and customize content based on prevailing trends and cultural nuances.
Synthesis : Text-to-Image Creation
Grounded in GenAI, synthesis enables the creation of images from textual descriptions. The applications stemming from these technologies are:
● Visual Search, which allows for the identification of similar items across the web from a picture.
● Fashion Image Captioning, which provides descriptions for uploaded attire images on platforms like Instagram.
● Fashion Attributing that extends to distinguishing genuine brands from counterfeits and understanding seasonal fashion attributes.
Once brands understand market trends and demands, designers can get to work.
Designing: Elevating Creativity and Authenticity with GenAI-Powered Tools
GenAI offers a toolkit that not only assists designers but can also augment their capabilities. CV can optimize patterns for minimal wastage and help formulate novel designs. Moreover, these AI tools are pivotal in brand creation and monitoring. Brands can leverage them to ensure their designs are unique and unreproduced by running visual similarity matches against other designs.
Such measures, exemplified by platforms like Fractal’s Imagine AI, safeguard against potential copying and validate the unique nature of designs before they’re introduced to the market. While AI can suggest iterations and multiple design versions, the fusion of human intuition with AI-driven insights truly refines the design process, ensuring authenticity and innovation in the final output.
Once a design that aligns with predicted trends and demands is created, brands create the physical product.
Enhanced Sourcing and Manufacturing through Computer Vision and Advanced Mapping Technologies
CV, combined with advanced mapping technologies, also plays a role in optimizing logistics, such as determining efficient routes from raw material sources to fabric-preparation factories.
Less than 2% of apparel used in the USA is domestically produced, with the bulk outsourced to countries like Bangladesh.
Poor sourcing often results in overproduction, driven by economic allure and fewer restrictions. The sourcing disparities raise crucial questions regarding the origin of raw materials and textiles. There’s an enormous potential for AI to bridge these knowledge gaps and enhance understanding of sourcing practices.
CV systems are employed in many textile mills, notably for quality control. As fabric progresses along the production line, it’s monitored by cameras detecting defects. Alerts are issued if a detected flaw surpasses a specific limit, ensuring only premium fabric proceeds further.
Maximizing Efficiency and Quality Assurance with Computer Vision
Optimization emerges as a vital challenge where CV plays an essential role. Imagine designing a shirt from a piece of cloth, where you must decide how to cut each segment to maximize fabric utilization and minimize waste. CV can strategize the most efficient cuts for the optimal use of fabric, as apparel sizes vary considerably.
CV is also indispensable in assessing the final garment against its original design. Pinpointing any discrepancies guarantees that the manufactured product accurately reflects the intended design, ensuring both quality and consistency.
The final step in the process is driving demand for the manufactured product.
Revolutionizing Personalized Branding and Fashion Styling with GenAI and 3D Models
A particularly compelling trend gaining traction is personalized brand creation with emerging brands. GenAI plays a pivotal role in this transformation, extracting data from online sources and generating suggestions tailored to individual preferences.
The concept of having an AI-based 3D representation of one’s body is also influencing personal styling. Paired with cloud or on-premise solutions, these AI models provide continuous, personalized fashion suggestions. From size and design recommendations to style advice based on current trends and events, AI promises a future where fashion choices are both intuitive and data-driven.
Stitching GenAI-driven Visual-language Personalized Experiences
The possibilities created by GenAI are extending into the realm of virtual try-ons. Initially conceived as booths digitizing one’s body for outfit suggestions, these try-ons are evolving to address challenges like sizing discrepancies across brands. The benefits range from cost savings in photoshoots to helping consumers visualize fits and designs before purchasing, ultimately reducing product returns.
Another emerging concept is the virtual mirror in retail environments. Instead of just reflecting, these mirrors provide a garment catalog, letting shoppers visualize clothes on them without trying them on physically.
All the innovations we’ve looked at in this article are transforming the industry and share the same technology. I call these innovations ‘Vision Foundation Models’. Their capabilities aren’t limited to visual interactivity; they’re amplified when combined with language. This blend of vision and language fosters an elevated user experience, creating a more interactive and integrated technological future.
Etching the Golden Thread: Foundation Models
Foundation models distinguish themselves from other AI by being pre-trained on vast data sets, extracting insights from massive volumes of information.
Initially, AI began with machine learning models using specific data for tasks. This journey evolved to deep learning models, which concentrate on high-level features, before advancing to foundation models, which learn tasks directly from data, amplifying generative AI’s capacities. They can perform various functions, from summarizing text to crafting poetry.
A pivotal moment for generative AI was Google’s 2017 ‘Transformers’ paper, “Attention is All You Need”, which ignited innovations in natural language processing and birthing models like GPT-3 and GPT-4. The vision community progressed a little later but rapidly advanced with models like Vision Transformer, CLIP, Swin, and Dino.
An innovative overlap is the fusion of vision and language, culminating in vision-language models. Techniques like ‘contrastive learning,’ which melds images with captions, have driven the rise of Vision Foundation models, which have harnessed robust image encoders integrated with extensive language models, using natural language as a guiding tool.
One burning question in AI has been achieving a model that can visualize and converse.
OpenAI’s CLIP model has begun to bridge this gap. Trained on 400 million image-text pairs, CLIP learns from both visual and textual data. Its capabilities include generating textual descriptions from images and visualizing an image from text. Its “zero-shot predictions” also enable it to make accurate classifications without additional fine-tuning, highlighting its proficiency in ‘zero-shot transfer.
Post-CLIP, the AI community has seen a surge in research, highlighting the expansive abilities of these models, which fluidly integrate vision and language in a bidirectional manner.
Fractal-forward into the Future of the Fashion Landscape
At Fractal, our expertise centers around the application domain, with systems capable of deep image analysis, identifying attributes like fabric patterns, necklines, and more, offering a comprehensive understanding of product attributes. We also use textual attributes from sources like social media and e-commerce sites, facilitating higher-order tasks like demand forecasting.
Our IVA platform underpins functionalities like inventory management and personalized recommendations, with attribute extraction models at its core.
Anticipating the future, the fashion industry’s transformation through AI hinges on the pioneering efforts of the CerebrAI team. Their focus lies in delving deep into ‘micro-stimuli,’ dissecting the critical final moments of consumer decision-making. This innovative approach operates on two levels: preserving the core identity of brands within novel designs while weaving in ‘micro-stimuli,’ discreet image elements that subtly sway consumer choices. In this endeavor, we aspire to contribute significantly to the fashion industry’s AI-powered evolution.