Zobrazeno 1 - 10
of 8 609
pro vyhledávání: '"Xie, Jun"'
This report presents our team's 'PCIE_LAM' solution for the Ego4D Looking At Me Challenge at CVPR2024. The main goal of the challenge is to accurately determine if a person in the scene is looking at the camera wearer, based on a video where the face
Externí odkaz:
http://arxiv.org/abs/2406.12211
Autor:
Zeng, Li, Huang, Haohan, Zheng, Binfan, Yang, Kang, Shao, Shengcheng, Zhou, Jinhua, Xie, Jun, Zhao, Rongqian, Chen, Xin
Graph Partitioning is widely used in many real-world applications such as fraud detection and social network analysis, in order to enable the distributed graph computing on large graphs. However, existing works fail to balance the computation cost an
Externí odkaz:
http://arxiv.org/abs/2403.00331
Autor:
Ning, Lin, Liu, Luyang, Wu, Jiaxing, Wu, Neo, Berlowitz, Devora, Prakash, Sushant, Green, Bradley, O'Banion, Shawn, Xie, Jun
Large language models (LLMs) have revolutionized natural language processing. However, effectively incorporating complex and potentially noisy user interaction data remains a challenge. To address this, we propose User-LLM, a novel framework that lev
Externí odkaz:
http://arxiv.org/abs/2402.13598
Publikováno v:
IEEE Transactions on Geoscience and Remote Sensing, vol. 61, 2023, pp. 1-12
LiDAR-camera fusion can enhance the performance of 3D object detection by utilizing complementary information between depth-aware LiDAR points and semantically rich images. Existing voxel-based methods face significant challenges when fusing sparse v
Externí odkaz:
http://arxiv.org/abs/2401.02702
Improving neural machine translation (NMT) systems with prompting has achieved significant progress in recent years. In this work, we focus on how to integrate multi-knowledge, multiple types of knowledge, into NMT models to enhance the performance w
Externí odkaz:
http://arxiv.org/abs/2312.04807
Expressing universal semantics common to all languages is helpful in understanding the meanings of complex and culture-specific sentences. The research theme underlying this scenario focuses on learning universal representations across languages with
Externí odkaz:
http://arxiv.org/abs/2310.17233
Autor:
Sun, Jie, Sun, Mo, Zhang, Zheng, Xie, Jun, Shi, Zuocheng, Yang, Zihan, Zhang, Jie, Wu, Fei, Wang, Zeke
Training graph neural networks (GNNs) on large-scale graph data holds immense promise for numerous real-world applications but remains a great challenge. Several disk-based GNN systems have been built to train large-scale graphs in a single machine.
Externí odkaz:
http://arxiv.org/abs/2310.00837
Autor:
Huang, Kaer, Sun, Bingchuan, Chen, Feng, Zhang, Tao, Xie, Jun, Li, Jian, Twombly, Christopher Walter, Wang, Zhepeng
In recent years, dominant Multi-object tracking (MOT) and segmentation (MOTS) methods mainly follow the tracking-by-detection paradigm. Transformer-based end-to-end (E2E) solutions bring some ideas to MOT and MOTS, but they cannot achieve a new state
Externí odkaz:
http://arxiv.org/abs/2308.01622
Autor:
Wei, Xiangpeng, Wei, Haoran, Lin, Huan, Li, Tianhao, Zhang, Pei, Ren, Xingzhang, Li, Mei, Wan, Yu, Cao, Zhiwei, Xie, Binbin, Hu, Tianxiang, Li, Shangjie, Hui, Binyuan, Yu, Bowen, Liu, Dayiheng, Yang, Baosong, Huang, Fei, Xie, Jun
Large language models (LLMs) demonstrate remarkable ability to comprehend, reason, and generate following nature language instructions. However, the development of LLMs has been primarily focused on high-resource languages, such as English, thereby l
Externí odkaz:
http://arxiv.org/abs/2307.06018
Autor:
Cao, Zhiwei, Yang, Baosong, Lin, Huan, Wu, Suhang, Wei, Xiangpeng, Liu, Dayiheng, Xie, Jun, Zhang, Min, Su, Jinsong
$k$-Nearest neighbor machine translation ($k$NN-MT) has attracted increasing attention due to its ability to non-parametrically adapt to new translation domains. By using an upstream NMT model to traverse the downstream training corpus, it is equippe
Externí odkaz:
http://arxiv.org/abs/2305.16599