MAVR-Net: Robust Multi-View Learning for MAV Action Recognition with Cross-View Attention

• URL: http://arxiv.org/abs/2510.15448v1
• Date: Fri, 17 Oct 2025 09:04:51 GMT
• Title: MAVR-Net: Robust Multi-View Learning for MAV Action Recognition with Cross-View Attention
• Authors: Nengbo Zhang, Hann Woei Ho,
• Abstract summary: This paper presents MAVR-Net, a multi-view learning-based MAV action recognition framework.<n>Unlike traditional single-view methods, the proposed approach combines three complementary types of data, including raw frames, optical flow, and RGB segmentation masks.<n>Specifically, ResNet-based encoders are used to extract discnative features from each view, and a multi-scale feature pyramid is adopted to preserve the details of MAV motion patterns.
• Score: 0.5156484100374058
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Recognizing the motion of Micro Aerial Vehicles (MAVs) is crucial for enabling cooperative perception and control in autonomous aerial swarms. Yet, vision-based recognition models relying only on RGB data often fail to capture the complex spatial temporal characteristics of MAV motion, which limits their ability to distinguish different actions. To overcome this problem, this paper presents MAVR-Net, a multi-view learning-based MAV action recognition framework. Unlike traditional single-view methods, the proposed approach combines three complementary types of data, including raw RGB frames, optical flow, and segmentation masks, to improve the robustness and accuracy of MAV motion recognition. Specifically, ResNet-based encoders are used to extract discriminative features from each view, and a multi-scale feature pyramid is adopted to preserve the spatiotemporal details of MAV motion patterns. To enhance the interaction between different views, a cross-view attention module is introduced to model the dependencies among various modalities and feature scales. In addition, a multi-view alignment loss is designed to ensure semantic consistency and strengthen cross-view feature representations. Experimental results on benchmark MAV action datasets show that our method clearly outperforms existing approaches, achieving 97.8\%, 96.5\%, and 92.8\% accuracy on the Short MAV, Medium MAV, and Long MAV datasets, respectively.

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