  While recent Transformer-based approaches have shown impressive performances
on event-based object detection tasks, their high computational costs still
diminish the low power consumption advantage of event cameras. Image-based
works attempt to reduce these costs by introducing sparse Transformers.
However, they display inadequate sparsity and adaptability when applied to
event-based object detection, since these approaches cannot balance the fine
granularity of token-level sparsification and the efficiency of window-based
Transformers, leading to reduced performance and efficiency. Furthermore, they
lack scene-specific sparsity optimization, resulting in information loss and a
lower recall rate. To overcome these limitations, we propose the Scene Adaptive
Sparse Transformer (SAST). SAST enables window-token co-sparsification,
significantly enhancing fault tolerance and reducing computational overhead.
Leveraging the innovative scoring and selection modules, along with the Masked
Sparse Window Self-Attention, SAST showcases remarkable scene-aware
adaptability: It focuses only on important objects and dynamically optimizes
sparsity level according to scene complexity, maintaining a remarkable balance
between performance and computational cost. The evaluation results show that
SAST outperforms all other dense and sparse networks in both performance and
efficiency on two large-scale event-based object detection datasets (1Mpx and
Gen1). Code: https://github.com/Peterande/SAST
