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The event camera has appealing properties: high dynamic range, low latency,low power consumption and low memory usage, and thus provides complementarinessto conventional frame-based cameras. It only captures the dynamics of a sceneand is able to capture almost "continuous" motion. However, different fromframe-based camera that reflects the whole appearance as scenes are, the eventcamera casts away the detailed characteristics of objects, such as texture andcolor. To take advantages of both modalities, the event camera and frame-basedcamera are combined together for various machine vision tasks. Then thecross-modal matching between neuromorphic events and color images plays a vitaland essential role. In this paper, we propose the Event-Based Image Retrieval(EBIR) problem to exploit the cross-modal matching task. Given an event streamdepicting a particular object as query, the aim is to retrieve color imagescontaining the same object. This problem is challenging because there exists alarge modality gap between neuromorphic events and color images. We address theEBIR problem by proposing neuromorphic Events-Color image Feature Learning(ECFL). Particularly, the adversarial learning is employed to jointly modelneuromorphic events and color images into a common embedding space. We alsocontribute to the community N-UKbench and EC180 dataset to promote thedevelopment of EBIR problem. Extensive experiments on our datasets show thatthe proposed method is superior in learning effective modality-invariantrepresentation to link two different modalities.

Matching Neuromorphic Events and Color Images via Adversarial Learning