Event-Based Video Reconstruction Using Transformer

Wenming Weng, Yueyi Zhang, Zhiwei Xiong; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 2563-2572


Event cameras, which output events by detecting spatio-temporal brightness changes, bring a novel paradigm to image sensors with high dynamic range and low latency. Previous works have achieved impressive performances on event-based video reconstruction by introducing convolutional neural networks (CNNs). However, intrinsic locality of convolutional operations is not capable of modeling long-range dependency, which is crucial to many vision tasks. In this paper, we present a hybrid CNN-Transformer network for event-based video reconstruction (ET-Net), which merits the fine local information from CNN and global contexts from Transformer. In addition, we further propose a Token Pyramid Aggregation strategy to implement multi-scale token integration for relating internal and intersected semantic concepts in the token-space. Experimental results demonstrate that our proposed method achieves superior performance over state-of-the-art methods on multiple real-world event datasets. The code is available at https://github.com/WarranWeng/ET-Net

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@InProceedings{Weng_2021_ICCV, author = {Weng, Wenming and Zhang, Yueyi and Xiong, Zhiwei}, title = {Event-Based Video Reconstruction Using Transformer}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)}, month = {October}, year = {2021}, pages = {2563-2572} }