Papers › TempSAL - Uncovering Temporal Information for Deep Saliency Prediction

TempSAL - Uncovering Temporal Information for Deep Saliency Prediction

1 Jan 2023CVPR 2023 1archive 2025-07-28

Bahar Aydemir, Ludo Hoffstetter, Tong Zhang, Mathieu Salzmann, Sabine Süsstrunk

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. Our approach locally modulates the saliency predictions by combining the learned temporal maps. Our experiments show that our method outperforms the state-of-the-art models, including a multi-duration saliency model, on the SALICON benchmark and CodeCharts1k dataset. Our code is publicly available on GitHub.

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Tasks

ObjectObject RecognitionPredictionSaliency DetectionSaliency Prediction

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Saliency Prediction SALECI TempSAL KL 0.712 #4 of 5 Archive leaderboard report
Saliency Prediction SALICON TempSAL AUC 0.869 #3 of 5 Archive leaderboard report
Saliency Prediction SALICON TempSAL CC 0.911 #3 of 5 Archive leaderboard report
Saliency Prediction SALICON TempSAL KLD 0.195 #3 of 5 Archive leaderboard report
Saliency Prediction SALICON TempSAL NSS 1.967 #3 of 5 Archive leaderboard report
Saliency Prediction SALICON TempSAL SIM 0.800 #3 of 5 Archive leaderboard report
Saliency Prediction SALICON TempSAL sAUC 0.745 #3 of 5 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

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