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Computer Vision and Machine Learning (MPI-INF D2)

Categories: Research  |  Pricing: Free  |  Official Website ↗

This research department focuses on advancing computer vision and machine learning techniques for robust perception and human-computer interaction.

The Computer Vision and Machine Learning department (D2) at the Max Planck Institute for Informatics, founded by Bernt Schiele in 2010, conducts research into perceptual computing and computer vision. The department aims to overcome limitations in robustness for vision algorithms, which are a bottleneck for industrial applications, by integrating multiple features and sensors. Their research encompasses areas such as people detection, pose estimation, tracking, visual privacy, adversarial robustness, zero-shot learning, generative models, vision and language, human activity recognition, knowledge transfer, semi-supervised learning, weakly supervised learning, image and video segmentation, object recognition, scene understanding, gaze-based human-computer interaction, and 3D reconstruction and perception of people.

Key Features

• People Detection, Pose Estimation and Tracking
• Visual Privacy
• Adversarial Robustness
• Zero-Shot Learning
• Generative Models
• Vision and Language
• Human Activity Recognition
• Knowledge Transfer and Semi-supervised Learning

Pros

• Conducts fundamental research in computer vision and machine learning
• Focuses on improving robustness of perception algorithms
• Explores a wide range of advanced AI topics
• Offers opportunities for academic applications (Postdoc, PhD, theses)
• Publishes research and provides software/datasets

Cons

• Not a commercial product or service
• Primarily an academic research department
• No direct pricing or commercial features
• Focus is on research, not immediate product deployment
• Limited to academic and research applications

Use Cases

• Advancing the state-of-the-art in computer vision
• Developing robust perception algorithms for robotics
• Improving human-computer interaction through visual cues
• Creating new generative models for images and 3D people
• Studying visual privacy and adversarial robustness

Best For

• Researchers
• Academics
• Students
• Other research institutions
• Developers interested in foundational AI research

Platforms: Web

Watch demo on YouTube ↗

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