SALICON Saliency Prediction Challenge (LSUN 2017)

Understanding and predicting visual attention in natural scenes is a challenging problem in vision. This challenge is designed to evaluate the performance of algorithms predicting visual saliency in natural scene images. The 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 Microsoft 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 dataset and their results will be compared against human behavioral data. We are looking forward to receiving submissions based on novel and context-aware saliency prediction models.