More complex deep learning models require more complex data, it's that simple
As this and other recent studies reflect, the pain is latent… deep learning models are getting very good at identifying objects in all kinds of scenes, however they can’t understand the relationships of those objects with each other and the surrounding environment.
Even simple relationships that are obvious for a human, like this is inside of this or that is on top of that, are very hard for widely used object detection and segmentation models. There is a growing number of use cases that will require this understanding.
The machine learning model MIT researchers have developed brings machines one step closer to understanding and interacting with the scene environment, just like humans would do…
This evolution in the models will require new data for training. One of the conclusions of the paper states:
Anyverse™ helps you continuously improve your deep learning perception models to reduce your system’s time to market applying new software 2.0 processes. Our synthetic data production platform allows us to provide high-fidelity accurate and balanced datasets. Along with a data-driven iterative process, we can help you reach the required model performance.
With Anyverse™ you can accurately simulate any camera sensor and help you decide which one will perform better with your perception system. No more complex and expensive experiments with real devices, thanks to our state-of-the-art photometric pipeline.
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