SICAF: Time-Varying Focus Bottleneck for Self-Supervised Event-Based Optical Flow with Spiking Neural Network

Summary

SICAF is a self-supervised training framework for spiking neural networks estimating event-based optical flow. It uses a constrained optimization with temporal modulation to compress latent representations without discarding temporal saliency, plus a learnable temporal attention estimator. The paper reports an 8.14% AEE improvement on MVSEC and a 13.49% robustness increase under white-box adversarial attacks.