Can you become a Computer Vision Research Scientist without a degree?

An alternative career path to becoming a Computer Vision Research Scientist with its major challenges, possible benefits, and some ways to hack your way into it.

It is possible to become a Computer Vision Research Scientist without a degree, but it can be more challenging compared to following a conventional academic path. While a degree can provide a strong foundation in the field, there are alternative routes to acquiring the necessary skills and knowledge.

Achieving a Career as a Computer Vision Research Scientist without a Degree:

1. Self-Study: Start by gaining a solid understanding of the fundamentals of computer vision, such as image processing, machine learning, and deep learning. There are numerous online resources available, including tutorials, courses, and textbooks, that can help you learn these concepts. Some popular online platforms for self-study include Coursera, Udacity, and edX.

2. Build a Strong Portfolio: Practical experience and projects are crucial for demonstrating your skills to potential employers. Implement computer vision algorithms, work on real-world datasets, and showcase your projects on platforms like GitHub. This will help you establish credibility and showcase your abilities to prospective employers.

3. Participate in Open Source Projects: Contribute to open-source computer vision projects to collaborate with experienced researchers and developers. This will not only enhance your technical skills but also provide you with valuable networking opportunities.

4. Attend Workshops and Conferences: Attend workshops, conferences, and meetups related to computer vision to stay updated with the latest research and network with professionals in the field. These events can provide valuable insights, foster collaborations, and help you gain visibility in the industry.

5. Collaborate with Researchers: Seek opportunities to collaborate with researchers or join research groups as a volunteer or intern. This will allow you to work on cutting-edge projects, gain practical experience, and learn from experts in the field.

Hacks and Advice:

1. Network and Connect: Actively engage with the computer vision community by participating in online forums, joining LinkedIn groups, and attending local meetups. Networking can lead to mentorship opportunities, job referrals, and collaborations.

2. Stay Updated: Computer vision is a rapidly evolving field, so it's essential to stay up-to-date with the latest research papers, publications, and advancements. Follow prominent researchers and organizations on platforms like arXiv, Medium, and Twitter to stay informed.

3. Contribute to Research: Publish your work in conferences or journals to establish yourself as a researcher. Collaborate with other researchers, submit your work to relevant conferences, and aim to contribute to the academic community.

Potential Difficulties and Benefits:

One of the main challenges of pursuing a career in computer vision without a degree is the lack of formal education, which can limit your access to certain job opportunities. Additionally, some employers may prioritize candidates with advanced degrees.

However, there are also benefits to this unconventional path. By focusing on practical experience and building a strong portfolio, you can demonstrate your skills and knowledge directly to potential employers. This can be particularly advantageous in industries that value practical skills and project-based experience.

Furthermore, the self-directed nature of this path allows for flexibility and the ability to tailor your learning to specific areas of interest. You can focus on the aspects of computer vision that align with your career goals, which may not be possible within a traditional academic curriculum.

In summary, while it is possible to become a Computer Vision Research Scientist without a degree, it requires dedication, self-study, practical experience, and networking. Building a strong portfolio, staying updated with the latest research, and actively participating in the computer vision community are essential steps to increase your chances of success.