TempSAL
Uncovering Temporal Information for Deep Saliency Prediction
Deep saliency prediction algorithms complement the object recognition features, they typically rely on additional information, such as scene context, semantic relationships, gaze direction, and object dissimilarity. However, none of these models consider the temporal nature of gaze shifts during image observation. We introduce a novel saliency prediction model that learns to output saliency maps in sequential time intervals by exploiting human temporal attention patterns.
inactive
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entered showcase: 2023-03-21
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entry updated: 2024-04-14
This project has not yet been evaluated by the C4DT Factory team.
We will be happy to evaluate it upon request.
Application, Experiments
MIT