Case study for emotion data annotation

Application fields of artificial intelligence is fast expanding, and the driving force behind this comes from the richness and diversity of datasets. Whether it is medical image analysis, autonomous driving or smart home systems, the accumulation of large amount of datasets provides infinite possibilities for AI application scenarios.

Nexdata, a leading global AI data asset company and data solution provider founded in 2011, provides high-qualitative GT training/validation data to all AI research & development across autonomous driving and intelligent cockpit.

As usual, Tech AD attracts world-leading companies and is an excellent high-end conference that looks into the future of autonomous driving. At its traditional location—Titanic center in Berlin, Germany—Nexdata will schedule the solution demo for the first time at  Tech.AD, from March 26-28 at Booth#36, in person and online. Nexdata's latest automotive data solutions will feature a series of perspectives and safety compliance levels. Visitors can take a self-guided tour to meet with high-quality AI training data and experience the safest data collection and labeling services.

Tech.AD Europe will comprehensively focus on trending topics such as autonomous vehicles, in-cabin systems, and ADAS and even discuss the latest industry policies, market trends, and the most advanced safety technologies and solutions. Nexdata will demonstrate the latest data solutions for Multi-Sensor Fusion Perception and In-Cabin Visual Perception. Their efficient annotation platform, powered by human-in-the-loop technology, will also be open for free trials. Visitors, no matter where they are located, will participate in the online portal for a simultaneous tour of the exhibition experience.

What's more, Nexdata will discuss the future of autonomous driving and data solutions to achieve a higher level with global industry leaders through participation in the Tech.AD Europe.

In the development of artificial intelligence, the importance of datasets are no substitute. For AI model to better understanding and predict human behavior, we have to ensure the integrity and diversity of data as prime mission. By pushing data sharing and data standardization construction, companies and research institutions will accelerate AI technologies maturity and popularity together.