Computer Vision News Computer Vision News 12 Oral & Award Candidate explains. “I want to overcome this assumption and use it more generally. Also, I want to utilize a convex relaxation technique for other applications in computer vision.” Moving forward, he wants to explore the fusion of classical geometric optimization methods with advanced learning-based techniques to develop more powerful solvers. “I have to think about it,” he ponders. “The combination is really hard. You have to learn both classical mathematics and keep up with the advanced techniques.” Bangyan highlights that many challenging fundamental problems in the field remain unsolved. If given the opportunity, he would love to solve them, particularly those related to convex relaxation and the scalability of semidefinite programming solvers. “As you can imagine, relaxation is to relax some assumptions,” he adds. “This relaxation might be tight or might be loose, and there’s no theory that can prove it entirely. They can only prove a tight relaxation under strict assumptions. Additionally, classical solvers have a significant scalability issue. At a very large scale, efficiency can’t be guaranteed. They can be really slow.” Last author Peidong Liu
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