for E-commerce and Advertisement Markets
A high-performance platform based on Neural Networks
is commercially used in several massive scenarios:
makes video a showcase of goods and services
- FeaturesAccurate and fast (in real time) visual content understanding, fashion recognition and fashion retrieval.
Configurable and flexible framework with support of hardware-based scalability.
Able working with GPUs of different types.
Distributed computing on servers and GPUs.
Ready-to-go pipeline.
API for simple integration.
- BenefitsProvides a new sales channel through the ability to handle impulsive purchases
Greatly increases the size of advertising properties
Improves engagement and retention by making ads less intrusive (comparing to mid-rolls and banners with behavior-based targeting)
Creates targeting possibilities in non-personalized channels (i.e. linear TV) via high-performance, real- time, automated, cost-effective labelling
Restores ads efficiency in the world of new privacy standards in the Internet
Enables a whole new array of services based on intelligent convergence between e-comm and media - Trusted
The engine was used to search for analogues of clothes from TV shows (in third-party app) and for contextual ad on HbbTV.
How it works:
- Object detection
- Video analysis
- Scene recognition
- Video fragments classification
- Video fragments matching with categories of goods and services to place relevant ads
- Special Features
The most common types of scenes in typical video content are recognized and associated with ad categories, for example food delivery, sporting goods, pets keeping, and café services.
High-accuracy scene type recognition.
- Object detection and tracking
- Person appearance matching with one of video characters
- Gender recognition
- Pose estimation
- Clothes recognition
- Features extraction for garments
- Similar clothing items search by video, based on features similarity
- Special Features
Variety of supported object types: clothes, shoes, hats, bags, and accessories, such as ties and glasses.
Able to work with millions of Stock Keeping Units in multiple shops, keeping the catalog up-to date on a daily basis.
High-accuracy recommendations of similar items.
based on product appearance analysis
AI tagging is an efficient alternative to manual tagging: number of tags per product is doubled with similar accuracy.
Thousand tags supported: category and model of clothing, shades of colors, texture, trim, material properties, style, gender, special attributes such as sleeve shape, heel height, etc.
Providing meaningful titles in a readable form.
140 color shades are recognized and can be used for filters and textual search.
SEARCH FOR SIMILAR CLOTHES
Similar offers search by image.
A product photo or any image or video uploaded by the user can be used as a search sample.
This efficient alternative to text search provides smart navigation in online fashion shops.
All detected garments in the uploaded photo can be used as inputs for retrieval enabling the user to recreate the whole outfit.
OUTFITTING RECOMMENDATIONS /
SEARCH FOR COMPLEMENTARY ITEMS
Complementary items search by image of reference clothes.
Generated combinations in clothes are perceived as advices from a human stylist.
Scalable solution: no manual labeling required.
Cross-selling as a benefit.
The generated image roughly predicts how the user would look in the selected dress. Two images required: photo of the desired dress, demonstrated by a model in an online shop, and user’s photo.
VTO gives customers the opportunity to see their image in the selected garments and to make informed decisions about purchase and so removes barriers to online shopping and reduces returns.
- Features
Variety of supported object types: clothes, shoes, hats, bags, and accessories, such as ties and glasses.
Variety of image types: product photos, images from the Internet, UGC, video.
High-accuracy recommendations of similar and complementary items.Real time processing.
Configurable and flexible framework with support of hardware-based scalability.
Able working with GPUs of different types.
Distributed computing on servers and GPUs.
Ready-to-go pipeline.
API for simple integration.
- BenefitsOnline shopping audience loyalty and engagement growth.
Cross-selling.
Sales and retention growth.
Identification of popular fashion trends based on image recognition. - TrustedTrusted by independent companies including two large European marketplaces.
Neural Networks, Deep Learning
Object detection
Object tracking in videos
Detection of a person’s appearance
Face detection
Gender recognition
Human pose estimation
Clothes detection, segmentation and recognition
Visual search for clothes (image search / video search): similar clothing retrieval for any garment by its image based on neural network features
Video scene analysis and recognition
Video fragments classification and matching with categories of goods and services for smart advertising
Video sentiment recognition (in progress)
Ready for use digital platform for image/video processing
CV expert in visual content recognition and Content-based Image Retrieval
specializing in fashion recognition,
with a focus on powering intelligent advertising and innovative sales systems
- First place in the clothing recognition competition: iMaterialist Challenge at FGVC 2017
- Two patents in Computer VisionHigh recognition accuracy and real-time performance characterize DressCoder products and solutions.
Please contact us at dresscoder.team@gmail.com
