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pro vyhledávání: '"xie, Jun"'
Achieving accurate and reliable gaze predictions in complex and diverse environments remains challenging. Fortunately, it is straightforward to access diverse gaze datasets in real-world applications. We discover that training these datasets jointly
Externí odkaz:
http://arxiv.org/abs/2409.04766
Autor:
Wu, Jiaxing, Ning, Lin, Liu, Luyang, Lee, Harrison, Wu, Neo, Wang, Chao, Prakash, Sushant, O'Banion, Shawn, Green, Bradley, Xie, Jun
LLM-powered personalization agent systems employ Large Language Models (LLMs) to predict users' behavior from their past activities. However, their effectiveness often hinges on the ability to effectively leverage extensive, long user historical data
Externí odkaz:
http://arxiv.org/abs/2409.04421
Autor:
Wang, Chao, Wu, Neo, Ning, Lin, Wu, Jiaxing, Liu, Luyang, Xie, Jun, O'Banion, Shawn, Green, Bradley
Large language models (LLMs) have shown remarkable capabilities in generating user summaries from a long list of raw user activity data. These summaries capture essential user information such as preferences and interests, and therefore are invaluabl
Externí odkaz:
http://arxiv.org/abs/2408.16966
Behavior cloning (BC) is a popular supervised imitation learning method in the societies of robotics, autonomous driving, etc., wherein complex skills can be learned by direct imitation from expert demonstrations. Despite its rapid development, it is
Externí odkaz:
http://arxiv.org/abs/2408.10568
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 achieved remarkable success across various domains, but effectively incorporating complex and potentially noisy user timeline data into LLMs remains a challenge. Current approaches often involve translating user time
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