Computer Vision News Computer Vision News 12 street images usually show very regular structures: one node, straight line, next node and then a bifurcation point. It depends on the country. This study used US cities as source domains so there usually is a very grid-like layout. What could add to this project? “We use this projection function for bridging the dimensionality gap,” explains Alex. “But what would be very interesting if we had a dimensionality agnostic network, i.e. a network that inherently can learn in 2D and 3D. This would also be beneficial for this time.” We had the chance to ask Johannes, the supervisor, what he's particularly proud about in this work. “What is incredible,” tells us Johannes “is that it is a master thesis project of Alexander. Initially he came to our lab as a master student. He's now pursuing a PhD. And just the fact that his first project, his initial master thesis project, became to be a paper and even an award-winning paper is an incredible achievement! From a scientific perspective, I find this task of not only detecting objects, but also identifying relationships between things in an image and solving this with a single neural network very interesting!” Back in the day Johannes and colleagues introduced the relation former framework, which was the first step that suffered from data scarcity problems. And now, taking this with transfer learning and domain adaptation to the next step is very logical to him. What he would find even more interesting is to go away from the pure physical structural graphs. For example, detecting cars as an additional object and therefore embedding more heterogeneous object representations. Similarly, in the medical domain, automatically predicting additional properties. So not only the structural graph, but maybe the radius or even the flow of a blood vessel. Best Student Paper Hon. Men.
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