3 accepted papers
In deep learning, functional similarity evaluation quantifies the extent to which independently trained models learn similar input–output relationships. In model stitching, functional similarity is framed as representation forward compatibility, i.e., whether the representations of two models can be…
Feature matching is a challenging computer vision task that involves finding correspondences between two images of a 3D scene. In this paper we consider the dense approach instead of the more common sparse paradigm, thus striving to find all correspondences. Perhaps counter-intuitively, dense method…
In contrast to the rapid digitalization of several industries, agriculture suffers from low adoption of smart farming tools. Even though recent advancements in AI-driven digital agriculture can offer high-performing predictive functionalities, they lack tangible quantitative evidence on their benefi…