architecture, the state of the art. In particular I'm really interested in hybrid architecture. Architectures that are based both on convolutional neural network but also transformers and to understand in particular if you can have a better identification of rare species when you use this type of architecture. I'm also collaborating with different universities to see if there are emerging technologies that can be applied. In particular, now we are trying to understand if the space of definition of the model is playing a role. If you use Euclidean space or if you use hyperbolic space or other spaces, is it really helping or not. I don't have the answer yet, but we are working for that. What is the most challenging part in your work that gives you the most headache to solve? Since it's highly interdisciplinary, I think that the most difficult part is to understand what is important and what is not important, because I'm a computer scientist, without any degree in biology; and so my point of view is always on the architecture, on the maths, on the computer vision side. I want to see what can I do more? How can I improve? How can I change and what is possible? But I never thought about if it's important or not, if it's useful. And on the other side I have to talk with biologists. They don't care about my architecture. They care about the result, because they want to have it to answer questions and I have to understand which type of answer, which type of result do they need and that is really, really difficult every time. Let’s rebound to your paper which unfortunately you're not presenting in person. What is this paper about and what we would have heard from you if you would have come to present? My paper is presenting a new architecture in computer vision that is using capsule layers to identify particular details in the images. That is the main idea: to understand if capsule layers can play a role in getting Women in Computer Vision 22 DAILY WACV Monday
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