SMILER: Saliency Model Implementation Library for Experimental Research
This provides a standardized framework for researchers in computer vision and saliency modeling, though it is incremental as it builds on existing models.
The authors tackled the problem of applying computational saliency models to new tasks and datasets by introducing SMILER, a software library that reduces human effort and ensures consistency, launching with 23 included models.
The Saliency Model Implementation Library for Experimental Research (SMILER) is a new software package which provides an open, standardized, and extensible framework for maintaining and executing computational saliency models. This work drastically reduces the human effort required to apply saliency algorithms to new tasks and datasets, while also ensuring consistency and procedural correctness for results and conclusions produced by different parties. At its launch SMILER already includes twenty three saliency models (fourteen models based in MATLAB and nine supported through containerization), and the open design of SMILER encourages this number to grow with future contributions from the community. The project may be downloaded and contributed to through its GitHub page: https://github.com/tsotsoslab/smiler