Papers › DeepVS: A Deep Learning Based Video Saliency Prediction Approach
DeepVS: A Deep Learning Based Video Saliency Prediction Approach
Lai Jiang, Mai Xu, Tie Liu, Minglang Qiao, Zulin Wang
In this paper, we propose a novel deep learning based video saliency prediction method, named DeepVS. Specifically, we establish a large-scale eye-tracking database of videos (LEDOV), which includes 32 subjects' fixations on 538 videos. We find from LEDOV that human attention is more likely to be attracted by objects, particularly the moving objects or the moving parts of objects. Hence, an object-to-motion convolutional neural network (OM-CNN) is developed to predict the intra-frame saliency for DeepVS, which is composed of the objectness and motion subnets. In OM-CNN, cross-net mask and hierarchical feature normalization are proposed to combine the spatial features of the objectness subnet and the temporal features of the motion subnet. We further find from our database that there exists a temporal correlation of human attention with a smooth saliency transition across video frames. We thus propose saliency-structured convolutional long short-term memory (SS-ConvLSTM) network, using the extracted features from OM-CNN as the input. Consequently, the inter-frame saliency maps of a video can be generated, which consider both structured output with center-bias and cross-frame transitions of human attention maps. Finally, the experimental results show that DeepVS advances the state-of-the-art in video saliency prediction.
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 | DeepVS | AUC-J | 0.804 | #12 of 14 | Archive leaderboard | report |
| Video Saliency Detection | MSU Video Saliency Prediction | DeepVS | CC | 0.586 | #12 of 14 | Archive leaderboard | report |
| Video Saliency Detection | MSU Video Saliency Prediction | DeepVS | FPS | 3.29 | #12 of 14 | Archive leaderboard | report |
| Video Saliency Detection | MSU Video Saliency Prediction | DeepVS | KLDiv | 0.707 | #12 of 14 | Archive leaderboard | report |
| Video Saliency Detection | MSU Video Saliency Prediction | DeepVS | NSS | 1.44 | #12 of 14 | Archive leaderboard | report |
| Video Saliency Detection | MSU Video Saliency Prediction | DeepVS | SIM | 0.548 | #12 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.
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