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Event cameras are novel sensors that perceive the per-pixel intensity changesand output asynchronous event streams with high dynamic range and less motionblur. It has been shown that events alone can be used for end-task learning,e.g., semantic segmentation, based on encoder-decoder-like networks. However,as events are sparse and mostly reflect edge information, it is difficult torecover original details merely relying on the decoder. Moreover, most methodsresort to pixel-wise loss alone for supervision, which might be insufficient tofully exploit the visual details from sparse events, thus leading to lessoptimal performance. In this paper, we propose a simple yet flexible two-streamframework named Dual Transfer Learning (DTL) to effectively enhance theperformance on the end-tasks without adding extra inference cost. The proposedapproach consists of three parts: event to end-task learning (EEL) branch,event to image translation (EIT) branch, and transfer learning (TL) module thatsimultaneously explores the feature-level affinity information and pixel-levelknowledge from the EIT branch to improve the EEL branch. This simple yet novelmethod leads to strong representation learning from events and is evidenced bythe significant performance boost on the end-tasks such as semanticsegmentation and depth estimation.

Dual Transfer Learning for Event-based End-task Prediction via Pluggable Event to Image Translation