Computer Vision News 14 Women in Computer Vision [hesitates a moment…] Perhaps yes, perhaps no. The interesting thing that we have is that a lot of the people who are doing the real research are researchers in training, if you want to see it like that. But let's say the interesting thing is when you look at what those people then later do, either they move on to industry and apply what they have [learned] to build crazy good products, or they actually move on to academia and start educating the next generation of researchers. In this sense, it makes sense that it's kind of like a selfreinforcing system. You already have a few years of research behind you. Maybe you want to tell me what you consider your best find till now. What are you most proud of? Well, I have done some data sets, and I'm still surprised that they are still around by now. I would have guessed that each of them would last probably for two to three years, and then they would be replaced by something way cooler. They are both still around, and I don’t know why. Oh, you can mention them! We are not shy. [laughs] Okay, I have done HMDB and Breakfast. It's actually very cool to see that people still find them useful. However, when you ask me what's the most important thing that I have done, honestly, it will always be one of my current projects. So, the current ones are always the most important to me, no matter what I have done in the past. What are the current projects? Can you share something with us? Yes, all the projects that I do at the moment are about multimodal learning. Technically, they all somehow deal with this question of how to bridge modalities. With this respect, many of them are actually not so much about building new architectures but understanding what current systems are doing and how to make this better. One of them is, for example, a paper that will be published at ICCV about learning by sorting. For example, we show that by changing the loss
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