EvDistill: Asynchronous Events to End-task Learning via Bidirectional Reconstruction-guided Cross-modal Knowledge Distillation

• URL: http://arxiv.org/abs/2111.12341v1
• Date: Wed, 24 Nov 2021 08:48:16 GMT
• Title: EvDistill: Asynchronous Events to End-task Learning via Bidirectional Reconstruction-guided Cross-modal Knowledge Distillation
• Authors: Lin Wang, Yujeong Chae, Sung-Hoon Yoon, Tae-Kyun Kim and Kuk-Jin Yoon
• Abstract summary: Event cameras sense per-pixel intensity changes and produce asynchronous event streams with high dynamic range and less motion blur. We propose a novel approach, called bfEvDistill, to learn a student network on the unlabeled and unpaired event data. We show that EvDistill achieves significantly better results than the prior works and KD with only events and APS frames.
• Score: 61.33010904301476
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Event cameras sense per-pixel intensity changes and produce asynchronous event streams with high dynamic range and less motion blur, showing advantages over conventional cameras. A hurdle of training event-based models is the lack of large qualitative labeled data. Prior works learning end-tasks mostly rely on labeled or pseudo-labeled datasets obtained from the active pixel sensor (APS) frames; however, such datasets' quality is far from rivaling those based on the canonical images. In this paper, we propose a novel approach, called \textbf{EvDistill}, to learn a student network on the unlabeled and unpaired event data (target modality) via knowledge distillation (KD) from a teacher network trained with large-scale, labeled image data (source modality). To enable KD across the unpaired modalities, we first propose a bidirectional modality reconstruction (BMR) module to bridge both modalities and simultaneously exploit them to distill knowledge via the crafted pairs, causing no extra computation in the inference. The BMR is improved by the end-tasks and KD losses in an end-to-end manner. Second, we leverage the structural similarities of both modalities and adapt the knowledge by matching their distributions. Moreover, as most prior feature KD methods are uni-modality and less applicable to our problem, we propose to leverage an affinity graph KD loss to boost the distillation. Our extensive experiments on semantic segmentation and object recognition demonstrate that EvDistill achieves significantly better results than the prior works and KD with only events and APS frames.

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