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

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.

Exploring deep learning for Event-Based Saliency Prediction with a Transformer-based model

Saliency prediction has been extensively studied in RGB images and videos as a computational model of human visual attention. In contrast, predicting saliency from event-based data remains largely unexplored, despite the biological inspiration and favorable sensing properties of event cameras. Two obstacles have held this direction back: the absence of large-scale event saliency datasets, and the lack of a strong baseline. In this paper, we introduce SEST (Swin Event-based Saliency Transformer), a transformer-based model for saliency prediction from event data, bridging the data scarcity barrier through event-native pretraining and synthetic supervision. SEST leverages a self-supervised pretrained event-based Swin Transformer backbone combined with a lightweight CNN decoder to produce dynamic saliency maps. To address the scarcity of annotated event-based saliency data, we introduce two new benchmark datasets, N-DHF1K and N-UCF Sports, generated from large-scale RGB saliency benchmarks. Experimental results show that SEST clearly outperforms existing event-based saliency methods and narrows the performance gap with state-of-the-art RGB models. Zero-shot evaluation on a real event camera dataset further demonstrates that our model trained on synthetic data remains transferable on real event streams. To the best of our knowledge, this work is the first to apply deep learning to event-based saliency prediction, opening a new research direction at the intersection of event-based vision and neuromorphic visual attention.