  Object detection with event cameras benefits from the sensor's low latency
and high dynamic range. However, it is costly to fully label event streams for
supervised training due to their high temporal resolution. To reduce this cost,
we present LEOD, the first method for label-efficient event-based detection.
Our approach unifies weakly- and semi-supervised object detection with a
self-training mechanism. We first utilize a detector pre-trained on limited
labels to produce pseudo ground truth on unlabeled events. Then, the detector
is re-trained with both real and generated labels. Leveraging the temporal
consistency of events, we run bi-directional inference and apply tracking-based
post-processing to enhance the quality of pseudo labels. To stabilize training
against label noise, we further design a soft anchor assignment strategy. We
introduce new experimental protocols to evaluate the task of label-efficient
event-based detection on Gen1 and 1Mpx datasets. LEOD consistently outperforms
supervised baselines across various labeling ratios. For example, on Gen1, it
improves mAP by 8.6% and 7.8% for RVT-S trained with 1% and 2% labels. On 1Mpx,
RVT-S with 10% labels even surpasses its fully-supervised counterpart using
100% labels. LEOD maintains its effectiveness even when all labeled data are
available, reaching new state-of-the-art results. Finally, we show that our
method readily scales to improve larger detectors as well. Code is released at
https://github.com/Wuziyi616/LEOD
