8 papers with code • 3 benchmarks • 4 datasets
Object segmentation is a crucial problem that is usually solved by using supervised learning approaches over very large datasets composed of both images and corresponding object masks.
However, since generative models are known to be unstable and sensitive to hyperparameters, the training of these methods can be challenging and time-consuming.
Moreover, object representations are often inferred using RNNs which do not scale well to large images or iterative refinement which avoids imposing an unnatural ordering on objects in an image but requires the a priori initialisation of a fixed number of object representations.
Ranked #1 on Unsupervised Object Segmentation on ObjectsRoom
Generative latent-variable models are emerging as promising tools in robotics and reinforcement learning.
Ranked #1 on Image Generation on Multi-dSprites
To force the generator to learn a representation where the foreground layer corresponds to an object, we perturb the output of the generative model by introducing a random shift of both the foreground image and mask relative to the background.