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DFKI presents new technology

Hida Winkle is a tech blogger from Ohio with a degree in mass communication and a gift for writing. She is the editor-in-chief of Hida’s favorite subjects are technology and building art. She is also a huge fan of Anime and Manga.

Machine pattern recognition

March 26, 2024

By Martin Ciupek

Reading time: approx. 3 minutes

Researchers at DFKI have developed a solution so that robots can better evaluate objects in context in the future. It will soon be presented in detail at a conference in the USA.

Robots equipped with numerous sensors already check the quality of components in automobile construction. In the future, the sensor data will also help machines to correctly interpret their environment like humans.
Photo: Mihajlo Maricic

Robots often have difficulty in everyday human environments. The reason: People evaluate what they see in a context. For example, if you see a full glass, you assume that liquid could spill over if you move roughly. So that machines can also learn to orient themselves visually in our living world, scientists at the German Research Center for Artificial Intelligence (DFKI) have developed the Multi-Key Anchor & Scene-Aware Transformer for 3D Visual Grounding (MiKASA). Using machine pattern recognition, it makes it possible to identify and semantically understand complex spatial dependencies and features of objects in three-dimensional space. At this year’s Conference on Computer Vision and Pattern Recognition (CVPR) in Seattle, USA, researchers from the Augmented Vision department are now presenting this solution.

Learning robots: Pattern recognition creates context for objects

People learn to put things into context when they learn language. This goes beyond pure language and helps, for example. B. to understand an intention or reference and to connect it with an object in our living environment. Robots do not yet have these skills, but they can learn them. There are research approaches around the world, but so far they have only come close to human capabilities.

Reading tip: Humanoid robot from Figure AI shows natural interaction thanks to ChatGPT

That is currently changing and the DFKI wants to play an important role with MiKASA. According to the researchers, thanks to a “scene-aware object recognizer”, machines can now draw conclusions from the surroundings of a reference object – and thus accurately recognize the object and define it correctly. So they evaluate things depending on the context and thus gain a nuanced understanding of their surroundings.

A question of perspective: Pitfalls in locating objects

However, the pitfalls lie in the details, for example when it comes to understanding relative spatial dependencies. “The chair in front of the blue monitor” can be “the chair behind the monitor” from another perspective. To make it clear to the machine that both chairs are actually one and the same object, the program works with a so-called “multi-key anchor concept”. This transmits the coordinates of anchor points in the field of view in relation to the target object and evaluates the importance of nearby objects based on text descriptions.

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