In this report, we present our champion solution for the NTIRE 2026 Challenge on Video Saliency Prediction held in conjunction with CVPR 2026. To exploit complementary inductive biases for video saliency, we propose Video Saliency with Adaptive Gated Experts (ViSAGE), a multi-expert ensemble framework. Each specialized decoder performs adaptive gating and modulation to refine spatio-temporal features. The complementary predictions from different experts are then fused at inference. ViSAGE thereby aggregates diverse inductive biases to capture complex spatio-temporal saliency cues in videos. On the Private Test set, ViSAGE ranked first on two out of four evaluation metrics, and outperformed most competing solutions on the other two metrics, demonstrating its effectiveness and generalization ability. Our code has been released at https://github.com/iLearn-Lab/CVPRW26-ViSAGE.
@article{arxiv.2604.08613,
title = {ViSAGE @ NTIRE 2026 Challenge on Video Saliency Prediction},
author = {Kun Wang and Yupeng Hu and Zhiran Li and Hao Liu and Qianlong Xiang and Liqiang Nie},
journal= {arXiv preprint arXiv:2604.08613},
year = {2026}
}