Zobrazeno 1 - 10
of 97
pro vyhledávání: '"Tang, Bohan"'
We introduce motion graph, a novel approach to the video prediction problem, which predicts future video frames from limited past data. The motion graph transforms patches of video frames into interconnected graph nodes, to comprehensively describe t
Externí odkaz:
http://arxiv.org/abs/2410.22288
Autor:
Tang, Shuo, Pang, Xianghe, Liu, Zexi, Tang, Bohan, Ye, Rui, Dong, Xiaowen, Wang, Yanfeng, Chen, Siheng
Post-training is essential for enabling large language models (LLMs) to follow human instructions. Inspired by the recent success of using LLMs to simulate human society, we leverage multi-agent simulation to automatically generate diverse text-based
Externí odkaz:
http://arxiv.org/abs/2410.14251
Hypergraphs are crucial for modelling higher-order interactions in real-world data. Hypergraph neural networks (HNNs) effectively utilise these structures by message passing to generate informative node features for various downstream tasks like node
Externí odkaz:
http://arxiv.org/abs/2402.05569
Hypergraphs play a pivotal role in the modelling of data featuring higher-order relations involving more than two entities. Hypergraph neural networks emerge as a powerful tool for processing hypergraph-structured data, delivering remarkable performa
Externí odkaz:
http://arxiv.org/abs/2312.11385
Hypergraphs are vital in modelling data with higher-order relations containing more than two entities, gaining prominence in machine learning and signal processing. Many hypergraph neural networks leverage message passing over hypergraph structures t
Externí odkaz:
http://arxiv.org/abs/2312.09778
Hypergraphs are important for processing data with higher-order relationships involving more than two entities. In scenarios where explicit hypergraphs are not readily available, it is desirable to infer a meaningful hypergraph structure from the nod
Externí odkaz:
http://arxiv.org/abs/2308.14172
Hypergraph structure learning, which aims to learn the hypergraph structures from the observed signals to capture the intrinsic high-order relationships among the entities, becomes crucial when a hypergraph topology is not readily available in the da
Externí odkaz:
http://arxiv.org/abs/2211.01717
Autor:
Tang, Bohan, Zhong, Yiqi, Xu, Chenxin, Wu, Wei-Tao, Neumann, Ulrich, Wang, Yanfeng, Zhang, Ya, Chen, Siheng
In multi-modal multi-agent trajectory forecasting, two major challenges have not been fully tackled: 1) how to measure the uncertainty brought by the interaction module that causes correlations among the predicted trajectories of multiple agents; 2)
Externí odkaz:
http://arxiv.org/abs/2207.05195
Demystifying the interactions among multiple agents from their past trajectories is fundamental to precise and interpretable trajectory prediction. However, previous works mainly consider static, pair-wise interactions with limited relational reasoni
Externí odkaz:
http://arxiv.org/abs/2206.13114
Uncertainty modeling is critical in trajectory forecasting systems for both interpretation and safety reasons. To better predict the future trajectories of multiple agents, recent works have introduced interaction modules to capture interactions amon
Externí odkaz:
http://arxiv.org/abs/2110.13947