Papers › Simple vs complex temporal recurrences for video saliency prediction
Simple vs complex temporal recurrences for video saliency prediction
Panagiotis Linardos, Eva Mohedano, Juan Jose Nieto, Noel E. O'Connor, Xavier Giro-i-Nieto, Kevin McGuinness
This paper investigates modifying an existing neural network architecture for static saliency prediction using two types of recurrences that integrate information from the temporal domain. The first modification is the addition of a ConvLSTM within the architecture, while the second is a conceptually simple exponential moving average of an internal convolutional state. We use weights pre-trained on the SALICON dataset and fine-tune our model on DHF1K. Our results show that both modifications achieve state-of-the-art results and produce similar saliency maps. Source code is available at https://git.io/fjPiB.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Video Saliency Detection | MSU Video Saliency Prediction | SalEMA | AUC-J | 0.821 | #9 of 14 | Archive leaderboard | report |
| Video Saliency Detection | MSU Video Saliency Prediction | SalEMA | CC | 0.636 | #9 of 14 | Archive leaderboard | report |
| Video Saliency Detection | MSU Video Saliency Prediction | SalEMA | FPS | 32.97 | #9 of 14 | Archive leaderboard | report |
| Video Saliency Detection | MSU Video Saliency Prediction | SalEMA | KLDiv | 0.647 | #9 of 14 | Archive leaderboard | report |
| Video Saliency Detection | MSU Video Saliency Prediction | SalEMA | NSS | 1.63 | #9 of 14 | Archive leaderboard | report |
| Video Saliency Detection | MSU Video Saliency Prediction | SalEMA | SIM | 0.571 | #9 of 14 | 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.
Methods
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