MCML - Perception, Vision, and Natural Language Processing

In the thriving era of Computer Vision, MCML researchers tackle key challenges by innovating beyond convolutional neural networks. They focus on novel models capturing both pixel relationships and high-level interactions, explore unsupervised learning techniques, and extend analysis beyond 2D to understand the 3D world observed through cameras.

Natural Language Processing (NLP) focuses on understanding and generating natural language text, greatly influenced by recent advances in deep learning. Despite substantial progress, our MCML researchers address key challenges like enhancing deep language understanding through structural biases, developing common sense in models through experimental environments, and improving sample efficiency for more effective learning from large datasets.

The ability for an intelligent, mobile actor to understand egomotion as well as the surroundings are a fundamental prerequisite for the choice of actions to take. However, vast challenges remain to achieve the necessary levels of safety, which are deeply rooted in research that MCML aims to carry out: Multisensor egomotion estimation and environment mapping, scene representations suitable for interaction in an open-ended environment, understanding and forecasting motion and events, and the the role of uncertainty in ML blocks as modular elements.