Zobrazeno 1 - 10
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pro vyhledávání: '"WANG, Hongjun"'
Spatiotemporal Graph Neural Networks (ST-GNNs) and Transformers have shown significant promise in traffic forecasting by effectively modeling temporal and spatial correlations. However, rapid urbanization in recent years has led to dynamic shifts in
Externí odkaz:
http://arxiv.org/abs/2411.11448
Cloth-changing person re-identification (CC-ReID) poses a significant challenge in computer vision. A prevailing approach is to prompt models to concentrate on causal attributes, like facial features and hairstyles, rather than confounding elements s
Externí odkaz:
http://arxiv.org/abs/2410.05536
Spatiotemporal neural networks have shown great promise in urban scenarios by effectively capturing temporal and spatial correlations. However, urban environments are constantly evolving, and current model evaluations are often limited to traffic sce
Externí odkaz:
http://arxiv.org/abs/2410.04740
Cloth-Changing Person Re-Identification (CC-ReID) involves recognizing individuals in images regardless of clothing status. In this paper, we empirically and experimentally demonstrate that completely eliminating or fully retaining clothing features
Externí odkaz:
http://arxiv.org/abs/2410.03977
Traffic forecasting is a cornerstone of smart city management, enabling efficient resource allocation and transportation planning. Deep learning, with its ability to capture complex nonlinear patterns in spatiotemporal (ST) data, has emerged as a pow
Externí odkaz:
http://arxiv.org/abs/2410.00385
Recent advancements in Spatiotemporal Graph Neural Networks (ST-GNNs) and Transformers have demonstrated promising potential for traffic forecasting by effectively capturing both temporal and spatial correlations. The generalization ability of spatio
Externí odkaz:
http://arxiv.org/abs/2410.00373
Detecting test-time distribution shift has emerged as a key capability for safely deployed machine learning models, with the question being tackled under various guises in recent years. In this paper, we aim to provide a consolidated view of the two
Externí odkaz:
http://arxiv.org/abs/2408.16757
Generalized Category Discovery (GCD) is a challenging task in which, given a partially labelled dataset, models must categorize all unlabelled instances, regardless of whether they come from labelled categories or from new ones. In this paper, we cha
Externí odkaz:
http://arxiv.org/abs/2408.04591
Global wildfire spreading dynamics and severity are analyzed using the susceptible-infected-recovered (SIR) compartment model. We use the novel FireTracks (FT) Scientific Dataset covering the wildfire time series of 2002-2023.
Externí odkaz:
http://arxiv.org/abs/2404.16874
Autor:
Zhang, Gengming, Cao, Hao, Hu, Kewei, Pan, Yaoqiang, Deng, Yuqin, Wang, Hongjun, Kang, Hanwen
Accurately identifying lychee-picking points in unstructured orchard environments and obtaining their coordinate locations is critical to the success of lychee-picking robots. However, traditional two-dimensional (2D) image-based object detection met
Externí odkaz:
http://arxiv.org/abs/2404.00364