Zobrazeno 1 - 10
of 118
pro vyhledávání: '"Xia, Jiahao"'
Object parts serve as crucial intermediate representations in various downstream tasks, but part-level representation learning still has not received as much attention as other vision tasks. Previous research has established that Vision Transformer c
Externí odkaz:
http://arxiv.org/abs/2408.08108
In frame-based vision, object detection faces substantial performance degradation under challenging conditions due to the limited sensing capability of conventional cameras. Event cameras output sparse and asynchronous events, providing a potential s
Externí odkaz:
http://arxiv.org/abs/2407.12582
Combining the message-passing paradigm with the global attention mechanism has emerged as an effective framework for learning over graphs. The message-passing paradigm and the global attention mechanism fundamentally generate node embeddings based on
Externí odkaz:
http://arxiv.org/abs/2407.02758
In this paper, a Segment Anything Model (SAM)-based pedestrian infrastructure segmentation workflow is designed and optimized, which is capable of efficiently processing multi-sourced geospatial data including LiDAR data and satellite imagery data. W
Externí odkaz:
http://arxiv.org/abs/2310.09918
Deep neural networks enable real-time monitoring of in-vehicle driver, facilitating the timely prediction of distractions, fatigue, and potential hazards. This technology is now integral to intelligent transportation systems. Recent research has expo
Externí odkaz:
http://arxiv.org/abs/2310.05202
Existing multiple modality fusion methods, such as concatenation, summation, and encoder-decoder-based fusion, have recently been employed to combine modality characteristics of Hyperspectral Image (HSI) and Light Detection And Ranging (LiDAR). Howev
Externí odkaz:
http://arxiv.org/abs/2304.00495
Heatmap regression methods have dominated face alignment area in recent years while they ignore the inherent relation between different landmarks. In this paper, we propose a Sparse Local Patch Transformer (SLPT) for learning the inherent relation. T
Externí odkaz:
http://arxiv.org/abs/2203.06541
Publikováno v:
In Neurocomputing 14 July 2024 590
Autor:
Younes, Hannah, Andrews, Clinton, Noland, Robert B., Xia, Jiahao, Wen, Song, Zhang, Wenwen, Metaxas, Dimitri, Von Hagen, Leigh Ann, Gong, Jie
Publikováno v:
In Journal of Urban Mobility June 2024 5
Autor:
Xia, Jiahao, Gong, Jie
Publikováno v:
In Automation in Construction March 2024 159