Search Results for author:
Found 2 papers, 2 papers with code
Unsupervised Multi-object Segmentation Using Attention and Soft-argmax
We introduce a new architecture for unsupervised object-centric representation learning and multi-object detection and segmentation, which uses a translation-equivariant attention mechanism to predict the coordinates of the objects present in the scene and to associate a feature vector to each object.
Ranked #1 on
Unsupervised Object Segmentation
on ClevrTex
Autoencoder-based background reconstruction and foreground segmentation with background noise estimation
The main novelty of the proposed model is that the autoencoder is also trained to predict the background noise, which allows to compute for each frame a pixel-dependent threshold to perform the foreground segmentation.
