CSEG: A COMPACT STATEFUL EVENT GRAPH REPRESENTATION FOR EFFICIENT VISUAL PROCESSING

The sparse, asynchronous output of event cameras is naturally suited to graph representations, yet treating each event as a node makes such graphs computationally expensive. Discarding events reduces this cost but inevitably loses information. Instead, we introduce CSEG, a compact stateful event graph representation that significantly reduces graph size while maintaining competitive detection accuracy. CSEG constructs a compact graph by retaining only a subset of event nodes, selected according to polarity and temporal information. For each dropped event node, local message differences guide the selection of a retained node as its fusion target. Its temporal information is then fused into this node through a temporal state update. We evaluate CSEG on event-based object detection using the Gen1 and DSEC-Det datasets. Compared with existing event graph methods, CSEG achieves competitive detection accuracies while retaining only 42.56% and 44.23% of event nodes on Gen1 and DSEC-Det datasets, respectively. Compared with the event graph baseline, CSEG achieves a 2.6x acceleration in inference, demonstrating its computational efficiency.