  This paper presents "Bina-Rep", a simple representation method that converts
asynchronous streams of events from event cameras to a sequence of sparse and
expressive event frames. By representing multiple binary event images as a
single frame of $N$-bit numbers, our method is able to obtain sparser and more
expressive event frames thanks to the retained information about event orders
in the original stream. Coupled with our proposed model based on a
convolutional neural network, the reported results achieve state-of-the-art
performance and repeatedly outperforms other common event representation
methods. Our approach also shows competitive robustness against common image
corruptions, compared to other representation techniques.
