FLAME: Fast Long-context Adaptive Memory for Event-based Vision

We propose Fast Long-context Adaptive Memory for Event (FLAME), a novel scalable architecture that combines neuro-inspired feature extraction with robust structured sequence modeling to efficiently process asynchronous and sparse event camera data. As a departure from conventional input encoding methods, FLAME presents Event Attention Layer, a novel feature extractor that leverages neuromorphic dynamics (Leaky Integrate-and-Fire (LIF)) to directly capture multi-timescale features from event streams. The feature extractor integrates with a structured state-space model with a novel Event-Aware HiPPO (EA-HiPPO) mechanism that dynamically adapts memory retention based on inter-event intervals to understand relationship across varying temporal scales and event sequences. ANormal Plus Low Rank (NPLR) decomposition reduces the computational complexity of state update from O(N2) to O(Nr), where N represents the dimension of the core state vector and r is the rank of a low-rank component (with r N). FLAME demonstrates state-of-the-art accuracy for event-by-event processing on complex event camera datasets.