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arXiv · 2502.00397

Minimalistic Video Saliency Prediction via Efficient Decoder & Spatio Temporal Action Cues

Rohit Girmaji·Siddharth Jain·Bhav Beri·Sarthak Bansal·Vineet Gandhi
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Abstract

This paper introduces ViNet-S, a 36MB model based on the ViNet architecture with a U-Net design, featuring a lightweight decoder that significantly reduces model size and parameters without compromising performance. Additionally, ViNet-A (148MB) incorporates spatio-temporal action localization (STAL) features, differing from traditional video saliency models that use action classification backbones. Our studies show that an ensemble of ViNet-S and ViNet-A, by averaging predicted saliency maps, achieves state-of-the-art performance on three visual-only and six audio-visual saliency datasets, outperforming transformer-based models in both parameter efficiency and real-time performance, with ViNet-S reaching over 1000fps.

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Rohit Girmaji, Siddharth Jain, Bhav Beri, Sarthak Bansal, Vineet Gandhi. 2025-02-01. Minimalistic Video Saliency Prediction via Efficient Decoder & Spatio Temporal Action Cues. https://arxiv.org/abs/2502.00397

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