Core Principles

While pursuing the application of state-of-the-art innovation from computer vision in commercial and industrial application DeepVISS aims to maintain the right to privacy, to security and to data autonomy of consumers and users. 

The five core principles which act as pillars DeepVISS:

• Foster Innovation. Exchanging ideas, approaches, data and capabilities between the research community and the industry leaders seeking to implement innovation with AI and computer vision.
• Discover and Share Opportunities. The research community is often plagued by small teams, working in virtual isolation and without access to capability to implement the interoperability, security and operational requirements of the industry. Our aim is to provide teams with the tools, resources and expertise to shorten the time to market of their innovation. Also, by preserving a priority of capabilities as demanded by industry stakeholders we can provide steering and guidance to future research efforts.
• Support the Research Community. One of our main goals is to offer better incentives and support to the research community. Innovation is often stifled and slowed down on its path from the laboratory to the market by the research teams having insufficient contact with industry needs, as well as by small teams being overburden with non-core technology and operational concerns.  
• Preserve a Clear Roadmap. We keep a close eye on the demands of the industry so as to be build a prioritized roadmap that guides future research effort. With all the hype peaking around computer vision, clients are confused by the myriad of algorithms and services, while researchers and developers often devote their effort and energy to what’s cool rather than to what’s useful.
• Ethical Computer Vision. A strong community is brought together not just by technology and opportunity, but also by values. We consider it a part of our core mission to educate the computer vision community about GDPR compliance, ethical use and general concerns of AI: fairness (lack of bias), data protection, transparency, openness and proportionality of purpose.