The pipeline language is quite general so that we can easily enrich AutoVideo with algorithms for various other video-related tasks in the future.
However, the attribution problem has not been well-defined, which lacks a unified guideline to the contribution assignment process.
The basic idea is to learn a source signal by back-propagation such that the mutual information between input and output should be as much as possible preserved in the mutual information between input and the source signal.
Attribution methods have been developed to understand the decision-making process of machine learning models, especially deep neural networks, by assigning importance scores to individual features.
Image captioning has made substantial progress with huge supporting image collections sourced from the web.
To further improve the graph representation learning ability, hierarchical GNN has been explored.
SpecAE leverages Laplacian sharpening to amplify the distances between representations of anomalies and the ones of the majority.
In this work, we propose the non-local U-Nets, which are equipped with flexible global aggregation blocks, for biomedical image segmentation.