Zobrazeno 1 - 10
of 473
pro vyhledávání: '"You, Xinge"'
Generalized additive models (GAM) have been successfully applied to high dimensional data analysis. However, most existing methods cannot simultaneously estimate the link function, the component functions and the variable interaction. To alleviate th
Externí odkaz:
http://arxiv.org/abs/2410.06012
Trajectory prediction is a crucial aspect of understanding human behaviors. Researchers have made efforts to represent socially interactive behaviors among pedestrians and utilize various networks to enhance prediction capability. Unfortunately, they
Externí odkaz:
http://arxiv.org/abs/2409.14984
Fine-grained recognition, a pivotal task in visual signal processing, aims to distinguish between similar subclasses based on discriminative information present in samples. However, prevailing methods often erroneously focus on background areas, negl
Externí odkaz:
http://arxiv.org/abs/2408.01998
Autor:
Wang, Wenjie, Huang, Biwei, Liu, Feng, You, Xinge, Liu, Tongliang, Zhang, Kun, Gong, Mingming
Score-based methods have demonstrated their effectiveness in discovering causal relationships by scoring different causal structures based on their goodness of fit to the data. Recently, Huang et al. proposed a generalized score function that can han
Externí odkaz:
http://arxiv.org/abs/2407.10132
Autor:
Hou, Wenjin, Chen, Shiming, Chen, Shuhuang, Hong, Ziming, Wang, Yan, Feng, Xuetao, Khan, Salman, Khan, Fahad Shahbaz, You, Xinge
Generative Zero-shot learning (ZSL) learns a generator to synthesize visual samples for unseen classes, which is an effective way to advance ZSL. However, existing generative methods rely on the conditions of Gaussian noise and the predefined semanti
Externí odkaz:
http://arxiv.org/abs/2404.14808
Object detection as a subfield within computer vision has achieved remarkable progress, which aims to accurately identify and locate a specific object from images or videos. Such methods rely on large-scale labeled training samples for each object ca
Externí odkaz:
http://arxiv.org/abs/2404.04799
Analyzing and forecasting trajectories of agents like pedestrians and cars in complex scenes has become more and more significant in many intelligent systems and applications. The diversity and uncertainty in socially interactive behaviors among a ri
Externí odkaz:
http://arxiv.org/abs/2310.05370
Few-shot object detection (FSOD) identifies objects from extremely few annotated samples. Most existing FSOD methods, recently, apply the two-stage learning paradigm, which transfers the knowledge learned from abundant base classes to assist the few-
Externí odkaz:
http://arxiv.org/abs/2309.08196
The challenge in fine-grained visual categorization lies in how to explore the subtle differences between different subclasses and achieve accurate discrimination. Previous research has relied on large-scale annotated data and pre-trained deep models
Externí odkaz:
http://arxiv.org/abs/2309.08097
In recent years, graph neural networks (GNN) have achieved significant developments in a variety of graph analytical tasks. Nevertheless, GNN's superior performance will suffer from serious damage when the collected node features or structure relatio
Externí odkaz:
http://arxiv.org/abs/2309.02762