Robust estimation of the essential matrix, which encodes the relative position and orientation of two cameras, is a fundamental step in structure from motion pipelines. Recent deep-based methods achieved accurate estimation by using complex network architectures that involve graphs, attention layers, and hard pruning steps.

To address these shortcomings, we introduce a pioneering technique that replaces the spatial model with a topological one within the RANSAC process.

In response, unsupervised learning approaches have emerged.

Neural Information Processing Systems http://nips.cc/

Recent methods introduce deep neural networks to predict the controlling parameters of hand-crafted geometric transformation models (e.g.

Object, attribute or stylistic changes can be learned from visually static datasets. On the other hand, high-quality data for action and reasoning-centric edits is scarce and has to come from entirely different sources that cover e.g.