Zobrazeno 1 - 10
of 1 781
pro vyhledávání: '"ZHANG Ziqi"'
Autor:
Deng, Zhaomeng, Zhang, Ziqi, Li, Ding, Guo, Yao, Ye, Yunfeng, Ren, Yuxin, Jia, Ning, Hu, Xinwei
Publikováno v:
2024 IEEE Real-Time Systems Symposium (RTSS), York, United Kingdom, 2024, pp. 308-321
Real-time operating systems employ spatial and temporal isolation to guarantee predictability and schedulability of real-time systems on multi-core processors. Any unbounded and uncontrolled cross-core performance interference poses a significant thr
Externí odkaz:
http://arxiv.org/abs/2412.18104
Autor:
Xu, Jingzehua, Xie, Guanwen, Zhang, Ziqi, Hou, Xiangwang, Ma, Dongfang, Zhang, Shuai, Ren, Yong, Niyato, Dusit
Publikováno v:
IEEE Transactions on Mobile Computing 2025
It is significant to employ multiple autonomous underwater vehicles (AUVs) to execute the underwater target tracking task collaboratively. However, it's pretty challenging to meet various prerequisites utilizing traditional control methods. Therefore
Externí odkaz:
http://arxiv.org/abs/2412.03959
This paper addresses key challenges in enhancing recommendation systems by leveraging Graph Neural Networks (GNNs) and addressing inherent limitations such as over-smoothing, which reduces model effectiveness as network hierarchy deepens. The propose
Externí odkaz:
http://arxiv.org/abs/2412.03097
This study proposes an automated data mining framework based on autoencoders and experimentally verifies its effectiveness in feature extraction and data dimensionality reduction. Through the encoding-decoding structure, the autoencoder can capture t
Externí odkaz:
http://arxiv.org/abs/2412.02211
Autor:
Hu, Huiyang, Wang, Peijin, Bi, Hanbo, Tong, Boyuan, Wang, Zhaozhi, Diao, Wenhui, Chang, Hao, Feng, Yingchao, Zhang, Ziqi, Ye, Qixiang, Fu, Kun, Sun, Xian
Remote sensing foundation models largely break away from the traditional paradigm of designing task-specific models, offering greater scalability across multiple tasks. However, they face challenges such as low computational efficiency and limited in
Externí odkaz:
http://arxiv.org/abs/2411.17984
Autor:
Zhang, Tao, Zhang, Ziqi, Ma, Zongyang, Chen, Yuxin, Qi, Zhongang, Yuan, Chunfeng, Li, Bing, Pu, Junfu, Zhao, Yuxuan, Xie, Zehua, Ma, Jin, Shan, Ying, Hu, Weiming
Advanced Multimodal Large Language Models (MLLMs) struggle with recent Knowledge-based VQA tasks, such as INFOSEEK and Encyclopedic-VQA, due to their limited and frozen knowledge scope, often leading to ambiguous and inaccurate responses. Thus, multi
Externí odkaz:
http://arxiv.org/abs/2411.15041
Trusted Execution Environments (TEE) are used to safeguard on-device models. However, directly employing TEEs to secure the entire DNN model is challenging due to the limited computational speed. Utilizing GPU can accelerate DNN's computation speed b
Externí odkaz:
http://arxiv.org/abs/2411.09945
Autor:
Zhou, Ce, Yan, Qiben, Kent, Daniel, Wang, Guangjing, Ding, Weikang, Zhang, Ziqi, Radha, Hayder
Monocular Depth Estimation (MDE) is a pivotal component of vision-based Autonomous Driving (AD) systems, enabling vehicles to estimate the depth of surrounding objects using a single camera image. This estimation guides essential driving decisions, s
Externí odkaz:
http://arxiv.org/abs/2411.00192
Monocular Depth Estimation (MDE) plays a crucial role in vision-based Autonomous Driving (AD) systems. It utilizes a single-camera image to determine the depth of objects, facilitating driving decisions such as braking a few meters in front of a dete
Externí odkaz:
http://arxiv.org/abs/2409.17376
Autor:
Chen, Yuxin, Ma, Zongyang, Zhang, Ziqi, Qi, Zhongang, Yuan, Chunfeng, Li, Bing, Pu, Junfu, Shan, Ying, Qi, Xiaojuan, Hu, Weiming
Dominant dual-encoder models enable efficient image-text retrieval but suffer from limited accuracy while the cross-encoder models offer higher accuracy at the expense of efficiency. Distilling cross-modality matching knowledge from cross-encoder to
Externí odkaz:
http://arxiv.org/abs/2407.07479