We represent temporal saliency by dividing fixations into time slices. Too many slices of data increase in-slice variance, reduce the number of fixations per slice, and hence reduce predictability. Using too few slices, on the other hand, restricts the observation of attention shifts.
In this paper, we extend Kadir and Brady's scale saliency model to quantify temporal saliency for performing automatic spatial and temporal scale selection. In ...
This work examines and proposes an extension to Kadir and Brady's Scale Saliency Algorithm for quantifying temporal saliency and performing automatic ...
We examine and propose an extension to Kadir and Brady's Scale Saliency Algorithm for quantifying temporal saliency and performing automatic spatial and ...
High Level Description of the Work: We address the problem of quantifying the uncertainty of detected saliency maps for videos. First, we study ...
We introduce a novel, saliency prediction model, namely TempSAL, capable of simultaneously predict- ing conventional image saliency and temporal saliency.
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Jul 21, 2022 · We devise a novel Temporal Saliency Query (TSQ) mechanism, which introduces class-specific information to provide fine-grained cues for saliency measurement.
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We can predict the temporal saliency maps for each interval separately, or combine them to create a single, refined image saliency map for the entire ...
In our work, we explore to what extent human gaze positions on recent video saliency benchmarks can be explained by static features. We apply models that cannot ...
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In this work, we test this assumption by quantifying to which extent gaze on recent video saliency benchmarks can be predicted by a static baseline model. On ...