SMILER: Saliency Model Implementation Library for Experimental Research

12/20/2018
by   Calden Wloka, et al.
4

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

READ FULL TEXT

page 1

page 2

page 3

page 4

research
05/31/2016

Quantitative Analysis of Saliency Models

Previous saliency detection research required the reader to evaluate per...
research
04/08/2022

ReservoirComputing.jl: An Efficient and Modular Library for Reservoir Computing Models

We introduce ReservoirComputing.jl, an open source Julia library for res...
research
07/11/2017

SaltiNet: Scan-path Prediction on 360 Degree Images using Saliency Volumes

We introduce SaltiNet, a deep neural network for scanpath prediction tra...
research
08/13/2023

UGC Quality Assessment: Exploring the Impact of Saliency in Deep Feature-Based Quality Assessment

The volume of User Generated Content (UGC) has increased in recent years...
research
10/01/2019

Research on insect pest image detection and recognition based on bio-inspired methods

Insect pest recognition is necessary for crop protection in many areas o...
research
09/07/2021

Datasets: A Community Library for Natural Language Processing

The scale, variety, and quantity of publicly-available NLP datasets has ...
research
10/11/2018

Bottom-up Attention, Models of

In this review, we examine the recent progress in saliency prediction an...

Please sign up or login with your details

Forgot password? Click here to reset