M^2C-EvDet: Multi-Domain Multi-Order Cross-Modal Knowledge Distillation for Event-based Object Detection

• URL: http://arxiv.org/abs/2606.24248v1
• Date: Tue, 23 Jun 2026 07:35:49 GMT
• Title: M^2C-EvDet: Multi-Domain Multi-Order Cross-Modal Knowledge Distillation for Event-based Object Detection
• Abstract summary: Event-based object detection (EvDet) demonstrates superior performance in scenarios demanding high temporal resolution and a wide dynamic.<n>This paper proposes M2C-EvDet, a Multi-domain and Multi-order Cross-modal knowledge distillation framework for EvDet.<n>Built upon frequency learning and hypergraph computation, M2C-EvDet integrates two specialized modules: Adaptive-Decoupled Feature Distillation (AF2D2) and Multi-Order Frequency Distillation (MORD)
• Score: 16.991308732308045
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Event-based object Detection (EvDet), as a biologically inspired visual perception paradigm, demonstrates superior performance in scenarios demanding high temporal resolution and a wide dynamic range. Nevertheless, the inherent sparse representations and inadequate visual semantics of event data result in a considerable performance disparity between EvDet and frame-based object detection. Previous works attempt to alleviate this cross-modal discrepancy through knowledge distillation, yet they only focus on spatial visual semantics or pair-wise relational information, thus limiting performance in more complex scenarios. To address this challenge, this paper proposes M^2C-EvDet, a Multi-domain and Multi-order Cross-modal knowledge distillation framework for EvDet. Built upon frequency learning and hypergraph computation, M^2C-EvDet integrates two specialized modules: Adaptive Frequency-Decoupled Feature Distillation (AF^2D^2) and Multi-Order Relational Distillation (MORD).

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