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CKSP: Cross-species Knowledge Sharing and Preserving for Universal Animal Activity Recognition

Autor: Mao, Axiu, Zhu, Meilu, Guo, Zhaojin, He, Zheng, Norton, Tomas, Liu, Kai

Deep learning techniques are dominating automated animal activity recognition (AAR) tasks with wearable sensors due to their high performance on large-scale labelled data. However, current deep learning-based AAR models are trained solely on datasets

Externí odkaz: http://arxiv.org/abs/2410.16644

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DEeR: Deviation Eliminating and Noise Regulating for Privacy-preserving Federated Low-rank Adaptation

Autor: Zhu, Meilu, Mao, Axiu, Liu, Jun, Yuan, Yixuan

Integrating low-rank adaptation (LoRA) with federated learning (FL) has received widespread attention recently, aiming to adapt pretrained foundation models (FMs) to downstream medical tasks via privacy-preserving decentralized training. However, owi

Externí odkaz: http://arxiv.org/abs/2410.12926

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Occlusion-Resistant Instance Segmentation of Piglets in Farrowing Pens Using Center Clustering Network

Autor: Huang, Endai, Mao, Axiu, Hou, Junhui, Wu, Yongjian, Xu, Weitao, Ceballos, Maria Camila, Parsons, Thomas D., Liu, Kai

Computer vision enables the development of new approaches to monitor the behavior, health, and welfare of animals. Instance segmentation is a high-precision method in computer vision for detecting individual animals of interest. This method can be us

Externí odkaz: http://arxiv.org/abs/2206.01942

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