Optical Flow Estimation is the problem of finding pixel-wise motions between consecutive images.
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MOFNet aims at computing and conveying the Optical Flow and a pixel-wise coding Mode selection.
Situational awareness and Indoor location tracking for firefighters is one of the tasks with paramount importance in search and rescue operations.
A key challenge for an agent learning to interact with the world is to reason about physical properties of objects and to foresee their dynamics under the effect of applied forces.
Many researches have been carried out for change detection using temporal SAR images.
Motivated by these findings, we propose a temporal multi-correspondence aggregation strategy to leverage similar patches across frames, and a cross-scale nonlocal-correspondence aggregation scheme to explore self-similarity of images across scales.
Cost volume is an essential component of recent deep models for optical flow estimation and is usually constructed by calculating the inner product between two feature vectors.
In order to further improve the architecture we introduce a weight function which aims to re-balance classes to increase the attention of the networks to under-represented objects.
Moreover, in the literature, it is seen that the use of different streams with informative input data helps to increase the performance in the recognition accuracy.
Recently, researchers in Machine Learning algorithms, Computer Vision scientists, engineers and others, showed a growing interest in 3D simulators as a mean to artificially create experimental settings that are very close to those in the real world.