SEST is the first deep learning model for event-based saliency prediction, using a pretrained Swin Transformer backbone and synthetic benchmarks to outperform prior event methods while transferring to real event streams.
In: ECCV (September 2018)
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Exploring deep learning for Event-Based Saliency Prediction with a Transformer-based model
SEST is the first deep learning model for event-based saliency prediction, using a pretrained Swin Transformer backbone and synthetic benchmarks to outperform prior event methods while transferring to real event streams.