Zobrazeno 1 - 10
of 2 856
pro vyhledávání: '"Tang, Chen"'
Autor:
Li, Yiheng, Ge, Chongjian, Li, Chenran, Xu, Chenfeng, Tomizuka, Masayoshi, Tang, Chen, Ding, Mingyu, Zhan, Wei
We propose Waymo Open Motion Dataset-Reasoning (WOMD-Reasoning), a language annotation dataset built on WOMD, with a focus on describing and reasoning interactions and intentions in driving scenarios. Previous language datasets primarily captured int
Externí odkaz:
http://arxiv.org/abs/2407.04281
Predicting the motion of multiple traffic participants has always been one of the most challenging tasks in autonomous driving. The recently proposed occupancy flow field prediction method has shown to be a more effective and scalable representation
Externí odkaz:
http://arxiv.org/abs/2407.01097
Autor:
Wang, Pengcheng, Li, Chenran, Weaver, Catherine, Kawamoto, Kenta, Tomizuka, Masayoshi, Tang, Chen, Zhan, Wei
Policies learned through Reinforcement Learning (RL) and Imitation Learning (IL) have demonstrated significant potential in achieving advanced performance in continuous control tasks. However, in real-world environments, it is often necessary to furt
Externí odkaz:
http://arxiv.org/abs/2407.00898
Named entity recognition (NER) stands as a fundamental and pivotal task within the realm of Natural Language Processing. Particularly within the domain of Biomedical Method NER, this task presents notable challenges, stemming from the continual influ
Externí odkaz:
http://arxiv.org/abs/2406.20038
Autor:
Chen, Lei, Meng, Yuan, Tang, Chen, Ma, Xinzhu, Jiang, Jingyan, Wang, Xin, Wang, Zhi, Zhu, Wenwu
Recent advancements in diffusion models, particularly the trend of architectural transformation from UNet-based Diffusion to Diffusion Transformer (DiT), have significantly improved the quality and scalability of image synthesis. Despite the incredib
Externí odkaz:
http://arxiv.org/abs/2406.17343
Autor:
Yang, Bohao, Liu, Dong, Tang, Chen, Xiao, Chenghao, Zhao, Kun, Li, Chao, Yuan, Lin, Yang, Guang, Huang, Lanxiao, Lin, Chenghua
Large Language Models (LLMs) possess the remarkable capability to understand human instructions and generate high-quality text, enabling them to act as agents that simulate human behaviours. This capability allows LLMs to emulate human beings in a mo
Externí odkaz:
http://arxiv.org/abs/2406.17962
Autor:
Zhao, Kun, Xiao, Chenghao, Tang, Chen, Yang, Bohao, Ye, Kai, Moubayed, Noura Al, Zhan, Liang, Lin, Chenghua
Radiology Report Generation (RRG) has achieved significant progress with the advancements of multimodal generative models. However, the evaluation in the domain suffers from a lack of fair and robust metrics. We reveal that, high performance on RRG w
Externí odkaz:
http://arxiv.org/abs/2406.17911
Autor:
Chen, Yuxin, Tang, Chen, Li, Chenran, Tian, Ran, Stone, Peter, Tomizuka, Masayoshi, Zhan, Wei
Aligning robot behavior with human preferences is crucial for deploying embodied AI agents in human-centered environments. A promising solution is interactive imitation learning from human intervention, where a human expert observes the policy's exec
Externí odkaz:
http://arxiv.org/abs/2406.16258
Autor:
Liu, Yijun, Meng, Yuan, Wu, Fang, Peng, Shenhao, Yao, Hang, Guan, Chaoyu, Tang, Chen, Ma, Xinzhu, Wang, Zhi, Zhu, Wenwu
Large language models (LLMs) have exhibited exciting progress in multiple scenarios, while the huge computational demands hinder their deployments in lots of real-world applications. As an effective means to reduce memory footprint and inference cost
Externí odkaz:
http://arxiv.org/abs/2406.12928
The creation of 4D avatars (i.e., animated 3D avatars) from text description typically uses text-to-image (T2I) diffusion models to synthesize 3D avatars in the canonical space and subsequently applies animation with target motions. However, such an
Externí odkaz:
http://arxiv.org/abs/2406.04629