# SED:Lightweight Saliency prediction for Event-based data via Distillation

arXiv: https://arxiv.org/abs/2606.14631 · HTML: https://mlsanity.com/paper/2606.14631/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 34 references that are in the map.
- First posted on arXiv 12 Jun 2026.

## Builds on (most cited first, 17 of 17)

- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [A Closer Look at Spatiotemporal Convolutions for Action Recognition](https://mlsanity.com/paper/1711.11248/) (2017)
- [What do different evaluation metrics tell us about saliency models?](https://mlsanity.com/paper/1604.03605/) (2016)
- [SalGAN: Visual Saliency Prediction with Generative Adversarial Networks](https://mlsanity.com/paper/1701.01081/) (2017)
- [Shallow and Deep Convolutional Networks for Saliency Prediction](https://mlsanity.com/paper/1603.00845/) (2016)
- [Joint Inference of Groups, Events and Human Roles in Aerial Videos](https://mlsanity.com/paper/1505.05957/) (2015)
- [SalFoM: Dynamic Saliency Prediction with Video Foundation Models](https://mlsanity.com/paper/2404.03097/) (2024)
- [3D Depthwise Convolution: Reducing Model Parameters in 3D Vision Tasks](https://mlsanity.com/paper/1808.01556/) (2018)
- [FastSal: a Computationally Efficient Network for Visual Saliency Prediction](https://mlsanity.com/paper/2008.11151/) (2020)
- [TinyHD: Efficient Video Saliency Prediction with Heterogeneous Decoders using Hierarchical Maps Distillation](https://mlsanity.com/paper/2301.04619/) (2023)
- [Diverse Temporal Aggregation and Depthwise Spatiotemporal Factorization for Efficient Video Classification](https://mlsanity.com/paper/2012.00317/) (2020)
- [Ultrafast Video Attention Prediction with Coupled Knowledge Distillation](https://mlsanity.com/paper/1904.04449/) (2019)
- [Spatiotemporal Knowledge Distillation for Efficient Estimation of Aerial Video Saliency](https://mlsanity.com/paper/1904.04992/) (2019)
- [An event-based implementation of saliency-based visual attention for rapid scene analysis](https://mlsanity.com/paper/2401.05030/) (2024)
- [Exploring deep learning for Event-Based Saliency Prediction with a Transformer-based model](https://mlsanity.com/paper/2605.23790/) (2026)

## Abstract (arXiv)

Event-based saliency prediction has gained attention recently, as combining event cameras with saliency estimation can act as an upstream stage that naturally improves the efficiency of downstream eventbased perception at the edge. However, current approaches are either neuromorphic, underperforming on event-based saliency benchmarks, or too heavy for resource-constrained edge applications due to their reliance on transformers or 3D convolutions. Drawing inspiration from efficient convolutional modules, SED and aiming to exploit the temporal information in event data, we propose a lightweight network, trained through knowledge distillation, built on a Depthwise Spatio-Temporal Block (DSTconv) -- a factorization of the 3D depthwise separable convolution. Relative to its teacher, our model reduces the model size from 180 MB to 0.32 MB (562x) and the parameter count from 45M to 81k (554x), while matching or outperforming it on the N-DHF1K and N-UCF Sports datasets. Moreover, it generalizes strongly beyond its training distribution, transferring from synthetic to real event data where a model trained from scratch fails.