Computer Vision News Computer Vision News 22 apply those models to the dataset in order to find some blind spots that we have in the dataset. I think that the team has been doing a great job adding features and features every day to the platform for making the platform more intuitive and easier to use. How immediate to work with it is? It is super easy to work with the documentation. It's intuitive. We have videos, we have images, we have 3D point clouds. Data can be very diverse. Does this work for health data? Does this work for sports data? Does this work with any data? Yes, this is this platform. The beauty of this platform is this is agnostic to the vertical or industry. We have projects that we can work in healthcare, oil and gas, retail, manufacturing, agriculture, LIDAR, autonomous vehicles… The company was born basically in an autonomous vehicles scenario. What do you do in the autonomous driving field? There are many things happening now. One of the main things that we have in autonomous vehicles is that we have multiple sensors to take the information of the car. You know, we have images, we have LIDAR information, we have Doppler information. With 51, it's possible to merge everything in the visualization. We are also launching a partnership that we have with NVIDIA. With NVIDIA, we can create this simulation, synthetic information, and put people, more people, cars, and also change the scenario. For example, if we are in summer, we can put the trees, green trees. If we are in fall, we can just put rain. Empty trees. Data can be good, it can be bad. What happens if the user comes with a data set, which is quite poor, and tries to organize it with your tool? Does it improve his data? Or does it give him more understanding about how bad his data is? That is a great question. Great, great question. Because normally we don't understand what kind of data we have and the quality of our data. We just trust in the protocols that we have to acquire data and believe that the protocols are okay. But then when we check, data is poor. And for sure, if you have poor data or bad data, you have bad models. So the models are not dumb because they are dumb. But when they use the platform, they can understand that they have bad data because we have a lot of indexes to qualify the quality of the data and the quality of the models. From the CVPR Expo
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