Zobrazeno 1 - 10
of 1 528
pro vyhledávání: '"chen, Jiming"'
This paper studies privacy-preserving resilient vector consensus in multi-agent systems against faulty agents, where normal agents can achieve consensus within the convex hull of their initial states while protecting state vectors from being disclose
Externí odkaz:
http://arxiv.org/abs/2411.03633
Multi-modal fusion is imperative to the implementation of reliable object detection and tracking in complex environments. Exploiting the synergy of heterogeneous modal information endows perception systems the ability to achieve more comprehensive, r
Externí odkaz:
http://arxiv.org/abs/2410.19872
Autor:
Du, Linkang, Zhou, Xuanru, Chen, Min, Zhang, Chusong, Su, Zhou, Cheng, Peng, Chen, Jiming, Zhang, Zhikun
As the implementation of machine learning (ML) systems becomes more widespread, especially with the introduction of larger ML models, we perceive a spring demand for massive data. However, it inevitably causes infringement and misuse problems with th
Externí odkaz:
http://arxiv.org/abs/2410.16618
Deep learning-based feature matching has shown great superiority for point cloud registration in the absence of pose priors. Although coarse-to-fine matching approaches are prevalent, the coarse matching of existing methods is typically sparse and lo
Externí odkaz:
http://arxiv.org/abs/2410.10295
Zero-shot (ZS) 3D anomaly detection is a crucial yet unexplored field that addresses scenarios where target 3D training samples are unavailable due to practical concerns like privacy protection. This paper introduces PointAD, a novel approach that tr
Externí odkaz:
http://arxiv.org/abs/2410.00320
Large language models (LLMs) have revolutionized natural language processing with their exceptional capabilities. However, deploying LLMs on resource-constrained edge devices presents significant challenges due to computational limitations, memory co
Externí odkaz:
http://arxiv.org/abs/2410.11845
Recent innovations in autonomous drones have facilitated time-optimal flight in single-drone configurations and enhanced maneuverability in multi-drone systems through the application of optimal control and learning-based methods. However, few studie
Externí odkaz:
http://arxiv.org/abs/2409.16720
Autor:
Chen, Jialuo, Wang, Jingyi, Zhang, Xiyue, Sun, Youcheng, Kwiatkowska, Marta, Chen, Jiming, Cheng, Peng
Due to the vast testing space, the increasing demand for effective and efficient testing of deep neural networks (DNNs) has led to the development of various DNN test case prioritization techniques. However, the fact that DNNs can deliver high-confid
Externí odkaz:
http://arxiv.org/abs/2409.09130
Recent advancements in sensor technology and deep learning have led to significant progress in 3D human body reconstruction. However, most existing approaches rely on data from a specific sensor, which can be unreliable due to the inherent limitation
Externí odkaz:
http://arxiv.org/abs/2409.04851
The millimeter-wave (mmWave) radar has been exploited for gesture recognition. However, existing mmWave-based gesture recognition methods cannot identify different users, which is important for ubiquitous gesture interaction in many applications. In
Externí odkaz:
http://arxiv.org/abs/2408.05358