Search Results for author:
Found 2 papers, 0 papers with code
Supersaliency: A Novel Pipeline for Predicting Smooth Pursuit-Based Attention Improves Generalizability of Video Saliency
To this end, we (i) use algorithmic and manual annotations of SP and fixations for two well-established video saliency data sets, (ii) train Slicing Convolutional Neural Networks for saliency prediction on either fixation- or SP-salient locations, and (iii) evaluate our and 26 publicly available dynamic saliency models on three data sets against traditional saliency and supersaliency ground truth.
Large-Scale Optimization of Hierarchical Features for Saliency Prediction in Natural Images
Our models outperform the state of the art on MIT1003, on which features and classifiers are learned.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
