The SALICON Challenge is designed to evaluate the performance of algorithms predicting visual saliency in natural images. Motivation of the challenge includes (1) to facilitate attention study in context and with non-iconic views, (2) to provide larger-scale human attentional data, and (3) to encourage the development of methods that leverage multiple annotation modalities from MS COCO. Saliency prediction results could in turn benefit other tasks like recognition and captioning – humans make multiple fixations to understand the visual input in natural scenes. Teams will be competing against each other by training their algorithms on the SALICON / MS COCO dataset and their results will be compared against human behavioral data.
2. Rules to Participate
Please submit through http://lsun.cs.princeton.
3. Tools and Instructions
Please follow the instructions in download, evaluation and upload for the data format and the best practice to upload results. The SALICON API and Evaluation Tools are released. The software provides the evaluation API and most common metrics, including AUC, Shuffled AUC, NSS and CC for algorithm development. The MATLAB version of these metrics can be found at the MIT saliency benchmark.