Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Yong, Silong"'
Volume rendering in neural radiance fields is inherently time-consuming due to the large number of MLP calls on the points sampled per ray. Previous works would address this issue by introducing new neural networks or data structures. In this work, W
Externí odkaz:
http://arxiv.org/abs/2410.19831
Autor:
Wan, Zifu, Zhang, Pingping, Wang, Yuhao, Yong, Silong, Stepputtis, Simon, Sycara, Katia, Xie, Yaqi
Multi-modal semantic segmentation significantly enhances AI agents' perception and scene understanding, especially under adverse conditions like low-light or overexposed environments. Leveraging additional modalities (X-modality) like thermal and dep
Externí odkaz:
http://arxiv.org/abs/2404.04256
Autor:
Huang, Jiangyong, Yong, Silong, Ma, Xiaojian, Linghu, Xiongkun, Li, Puhao, Wang, Yan, Li, Qing, Zhu, Song-Chun, Jia, Baoxiong, Huang, Siyuan
Leveraging massive knowledge from large language models (LLMs), recent machine learning models show notable successes in general-purpose task solving in diverse domains such as computer vision and robotics. However, several significant challenges rem
Externí odkaz:
http://arxiv.org/abs/2311.12871
Autor:
Ma, Xiaojian, Yong, Silong, Zheng, Zilong, Li, Qing, Liang, Yitao, Zhu, Song-Chun, Huang, Siyuan
We propose a new task to benchmark scene understanding of embodied agents: Situated Question Answering in 3D Scenes (SQA3D). Given a scene context (e.g., 3D scan), SQA3D requires the tested agent to first understand its situation (position, orientati
Externí odkaz:
http://arxiv.org/abs/2210.07474
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