Object-centric Cross-modal Feature Distillation for Event-based Object
  Detection
Lei Li Alexander Liniger Mario Millhaeusler Vagia Tsiminaki Yuanyou Li Dengxin Dai
Event cameras are gaining popularity due to their unique properties, such as
their low latency and high dynamic range. One task where these benefits can be
crucial is real-time object detection. However, RGB detectors still outperform
event-based detectors due to the sparsity of the event data and missing visual
details. In this paper, we develop a novel knowledge distillation approach to
shrink the performance gap between these two modalities. To this end, we
propose a cross-modality object detection distillation method that by design
can focus on regions where the knowledge distillation works best. We achieve
this by using an object-centric slot attention mechanism that can iteratively
decouple features maps into object-centric features and corresponding
pixel-features used for distillation. We evaluate our novel distillation
approach on a synthetic and a real event dataset with aligned grayscale images
as a teacher modality. We show that object-centric distillation allows to
significantly improve the performance of the event-based student object
detector, nearly halving the performance gap with respect to the teacher.