Computer Vision

The Computer Vision Chapter is the ML6 expert group on all things related to Computer Vision.

As the ML6 special unit for computer vision, our goal is to keep in touch with the latest developments in the field and share our learnings with colleagues, customers, the open-source community, and the public at large. Some areas we are active in at the moment are Object Detection, Video Analysis, Generative AI, Edge Vision and Visual Inspection.

Object detection

Develop custom, high-performance machine learning models for detecting objects at high speed, at high resolutions, and in challenging real-world circumstances.  Different use cases demand different approaches to data pre-processing, modeling, tuning and setup.

Video analysis

Use object tracking across frames to support object detection and segmentation. Detect phenomena or activities that can only be recognized taking the entire stream of images into account. Video analysis presents unique challenges in terms of resource management and model architecture.

Generative AI

Neural networks can transfer faces, poses, stylistic attributes or can generate unseen instances of faces, people, objects or even artworks based on examples.We are only scratching the surface of the potential in generative modeling in media but also design, retail and other areas. For more information, visit gener8.ai

Edge Vision

Processing video on edge, near the camera can reduce network traffic and increase data security. Example applications include a high-performing solution for anonymization and re-identification on edge. Edge processing presents a number of challenges in terms of performance, architecture, ops and security.

Visual inspection

Vision based quality control and assurance, based on the latest advancements in machine vision. With machine learning, we can detect a wide range of defects on a diverse set of products. Using these SOTA algorithms, production processes can be monitored, steered and optimized.

Demos

How to spot a deepfake ?

This video explains and illustrates the small clues that can help you distinguish deepfakes from real videos.

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An Unbeatable Rock-Paper-Scissors AI Robot

Learn how we've built an unbeatable robothand

MLSox explained : How to create a sock matching application from scratch using YOLOv4 and siamese networks

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Jeroom dancing through pose transfer ( with VT4/GoPlay)

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Case Studies & Research

Internships

Video Analysis
Image-based anomaly detection
SMOG sign language recognition with Google Glass
Content GANeration
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