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pro vyhledávání: '"Zhang, Qiming"'
Autor:
Peng, Mingxing, Chen, Kehua, Guo, Xusen, Zhang, Qiming, Lu, Hongliang, Zhong, Hui, Chen, Di, Zhu, Meixin, Yang, Hai
Intelligent Transportation Systems (ITS) are vital in modern traffic management and optimization, significantly enhancing traffic efficiency and safety. Recently, diffusion models have emerged as transformative tools for addressing complex challenges
Externí odkaz:
http://arxiv.org/abs/2409.15816
Recent advancements in deep learning have greatly advanced the field of infrared small object detection (IRSTD). Despite their remarkable success, a notable gap persists between these IRSTD methods and generic segmentation approaches in natural image
Externí odkaz:
http://arxiv.org/abs/2409.04714
Due to spatial redundancy in remote sensing images, sparse tokens containing rich information are usually involved in self-attention (SA) to reduce the overall token numbers within the calculation, avoiding the high computational cost issue in Vision
Externí odkaz:
http://arxiv.org/abs/2405.09789
Traffic forecasting is crucial for intelligent transportation systems. It has experienced significant advancements thanks to the power of deep learning in capturing latent patterns of traffic data. However, recent deep-learning architectures require
Externí odkaz:
http://arxiv.org/abs/2404.02937
The aspiration of the next generation's autonomous driving (AD) technology relies on the dedicated integration and interaction among intelligent perception, prediction, planning, and low-level control. There has been a huge bottleneck regarding the u
Externí odkaz:
http://arxiv.org/abs/2401.12888
Single hyperspectral image super-resolution (single-HSI-SR) aims to restore a high-resolution hyperspectral image from a low-resolution observation. However, the prevailing CNN-based approaches have shown limitations in building long-range dependenci
Externí odkaz:
http://arxiv.org/abs/2307.14010
Autor:
Chen, Tao, Lv, Liang, Wang, Di, Zhang, Jing, Yang, Yue, Zhao, Zeyang, Wang, Chen, Guo, Xiaowei, Chen, Hao, Wang, Qingye, Xu, Yufei, Zhang, Qiming, Du, Bo, Zhang, Liangpei, Tao, Dacheng
With the world population rapidly increasing, transforming our agrifood systems to be more productive, efficient, safe, and sustainable is crucial to mitigate potential food shortages. Recently, artificial intelligence (AI) techniques such as deep le
Externí odkaz:
http://arxiv.org/abs/2305.01899
Multi-view camera-based 3D object detection has become popular due to its low cost, but accurately inferring 3D geometry solely from camera data remains challenging and may lead to inferior performance. Although distilling precise 3D geometry knowled
Externí odkaz:
http://arxiv.org/abs/2303.16818
Window-based attention has become a popular choice in vision transformers due to its superior performance, lower computational complexity, and less memory footprint. However, the design of hand-crafted windows, which is data-agnostic, constrains the
Externí odkaz:
http://arxiv.org/abs/2303.15105
In this paper, we show the surprisingly good properties of plain vision transformers for body pose estimation from various aspects, namely simplicity in model structure, scalability in model size, flexibility in training paradigm, and transferability
Externí odkaz:
http://arxiv.org/abs/2212.04246