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pro vyhledávání: '"Zhang, Qiming"'
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
Autor:
Kiefer, Benjamin, Kristan, Matej, Perš, Janez, Žust, Lojze, Poiesi, Fabio, Andrade, Fabio Augusto de Alcantara, Bernardino, Alexandre, Dawkins, Matthew, Raitoharju, Jenni, Quan, Yitong, Atmaca, Adem, Höfer, Timon, Zhang, Qiming, Xu, Yufei, Zhang, Jing, Tao, Dacheng, Sommer, Lars, Spraul, Raphael, Zhao, Hangyue, Zhang, Hongpu, Zhao, Yanyun, Augustin, Jan Lukas, Jeon, Eui-ik, Lee, Impyeong, Zedda, Luca, Loddo, Andrea, Di Ruberto, Cecilia, Verma, Sagar, Gupta, Siddharth, Muralidhara, Shishir, Hegde, Niharika, Xing, Daitao, Evangeliou, Nikolaos, Tzes, Anthony, Bartl, Vojtěch, Špaňhel, Jakub, Herout, Adam, Bhowmik, Neelanjan, Breckon, Toby P., Kundargi, Shivanand, Anvekar, Tejas, Desai, Chaitra, Tabib, Ramesh Ashok, Mudengudi, Uma, Vats, Arpita, Song, Yang, Liu, Delong, Li, Yonglin, Li, Shuman, Tan, Chenhao, Lan, Long, Somers, Vladimir, De Vleeschouwer, Christophe, Alahi, Alexandre, Huang, Hsiang-Wei, Yang, Cheng-Yen, Hwang, Jenq-Neng, Kim, Pyong-Kun, Kim, Kwangju, Lee, Kyoungoh, Jiang, Shuai, Li, Haiwen, Ziqiang, Zheng, Vu, Tuan-Anh, Nguyen-Truong, Hai, Yeung, Sai-Kit, Jia, Zhuang, Yang, Sophia, Hsu, Chih-Chung, Hou, Xiu-Yu, Jhang, Yu-An, Yang, Simon, Yang, Mau-Tsuen
The 1$^{\text{st}}$ Workshop on Maritime Computer Vision (MaCVi) 2023 focused on maritime computer vision for Unmanned Aerial Vehicles (UAV) and Unmanned Surface Vehicle (USV), and organized several subchallenges in this domain: (i) UAV-based Maritim
Externí odkaz:
http://arxiv.org/abs/2211.13508
Self-supervised pre-training vision transformer (ViT) via masked image modeling (MIM) has been proven very effective. However, customized algorithms should be carefully designed for the hierarchical ViTs, e.g., GreenMIM, instead of using the vanilla
Externí odkaz:
http://arxiv.org/abs/2211.01785