TempSAL - Uncovering Temporal Information for Deep Saliency Prediction

An example of how human attention shifts over time. We show the input image and the corresponding image saliency ground truth from the SALICON dataset. Notice that in $\mathcal{T_1}$, the cook is salient, while in $\mathcal{T_2}$ and $\mathcal{T_3}$, the food on the barbecue becomes the most salient region in this scene. We can predict saliency maps in these sequential time intervals as well as combine them into a refined single image saliency map for the whole observation duration.

We will relase the code on this repository upon publication.

Paper link : https://arxiv.org/abs/2301.02315

Project page and Supplementary material : https://ivrl.github.io/Tempsal/

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