Zobrazeno 1 - 10
of 836
pro vyhledávání: '"CHEN Minghao"'
Autor:
Yang, Yanting, Chen, Minghao, Qiu, Qibo, Wu, Jiahao, Wang, Wenxiao, Lin, Binbin, Guan, Ziyu, He, Xiaofei
For a general-purpose robot to operate in reality, executing a broad range of instructions across various environments is imperative. Central to the reinforcement learning and planning for such robotic agents is a generalizable reward function. Recen
Externí odkaz:
http://arxiv.org/abs/2407.14872
Large Language Models (LLM) based agents have shown promise in autonomously completing tasks across various domains, e.g., robotics, games, and web navigation. However, these agents typically require elaborate design and expert prompts to solve tasks
Externí odkaz:
http://arxiv.org/abs/2405.16247
Autor:
Zhang, Zhanwei, Chen, Minghao, Xiao, Shuai, Peng, Liang, Li, Hengjia, Lin, Binbin, Li, Ping, Wang, Wenxiao, Wu, Boxi, Cai, Deng
Recent self-training techniques have shown notable improvements in unsupervised domain adaptation for 3D object detection (3D UDA). These techniques typically select pseudo labels, i.e., 3D boxes, to supervise models for the target domain. However, t
Externí odkaz:
http://arxiv.org/abs/2404.19384
Predicting future trajectories of traffic agents accurately holds substantial importance in various applications such as autonomous driving. Previous methods commonly infer all future steps of an agent either recursively or simultaneously. However, t
Externí odkaz:
http://arxiv.org/abs/2404.19330
We consider the problem of editing 3D objects and scenes based on open-ended language instructions. A common approach to this problem is to use a 2D image generator or editor to guide the 3D editing process, obviating the need for 3D data. However, t
Externí odkaz:
http://arxiv.org/abs/2404.18929
Autor:
Chen, Minghao
With the rapid advancement of technology, the recognition of underwater acoustic signals in complex environments has become increasingly crucial. Currently, mainstream underwater acoustic signal recognition relies primarily on time-frequency analysis
Externí odkaz:
http://arxiv.org/abs/2312.13143
Autor:
Lin, Yuqi, Chen, Minghao, Zhang, Kaipeng, Li, Hengjia, Li, Mingming, Yang, Zheng, Lv, Dongqin, Lin, Binbin, Liu, Haifeng, Cai, Deng
Contrastive Language-Image Pre-training (CLIP) has demonstrated impressive capabilities in open-vocabulary classification. The class token in the image encoder is trained to capture the global features to distinguish different text descriptions super
Externí odkaz:
http://arxiv.org/abs/2312.12828
We propose a novel feed-forward 3D editing framework called Shap-Editor. Prior research on editing 3D objects primarily concentrated on editing individual objects by leveraging off-the-shelf 2D image editing networks. This is achieved via a process c
Externí odkaz:
http://arxiv.org/abs/2312.09246
Unsupervised domain adaptation (UDA) methods facilitate the transfer of models to target domains without labels. However, these methods necessitate a labeled target validation set for hyper-parameter tuning and model selection. In this paper, we aim
Externí odkaz:
http://arxiv.org/abs/2308.00287
Autor:
Yang, Honghui, Wang, Wenxiao, Chen, Minghao, Lin, Binbin, He, Tong, Chen, Hua, He, Xiaofei, Ouyang, Wanli
Recent Transformer-based 3D object detectors learn point cloud features either from point- or voxel-based representations. However, the former requires time-consuming sampling while the latter introduces quantization errors. In this paper, we present
Externí odkaz:
http://arxiv.org/abs/2305.06621