Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Zhang, Juexiao"'
A proper scene representation is central to the pursuit of spatial intelligence where agents can robustly reconstruct and efficiently understand 3D scenes. A scene representation is either metric, such as landmark maps in 3D reconstruction, 3D boundi
Externí odkaz:
http://arxiv.org/abs/2410.11187
Vision Language Models (VLMs) have recently been adopted in robotics for their capability in common sense reasoning and generalizability. Existing work has applied VLMs to generate task and motion planning from natural language instructions and simul
Externí odkaz:
http://arxiv.org/abs/2410.08792
Large language models (LLMs) exhibit a variety of promising capabilities in robotics, including long-horizon planning and commonsense reasoning. However, their performance in place recognition is still underexplored. In this work, we introduce multim
Externí odkaz:
http://arxiv.org/abs/2406.17520
Autor:
Zhang, Jing, Fang, Irving, Zhang, Juexiao, Wu, Hao, Kaushik, Akshat, Rodriguez, Alice, Zhao, Hanwen, Zheng, Zhuo, Iovita, Radu, Feng, Chen
Lithic Use-Wear Analysis (LUWA) using microscopic images is an underexplored vision-for-science research area. It seeks to distinguish the worked material, which is critical for understanding archaeological artifacts, material interactions, tool func
Externí odkaz:
http://arxiv.org/abs/2403.13171
Collaborative perception leverages rich visual observations from multiple robots to extend a single robot's perception ability beyond its field of view. Many prior works receive messages broadcast from all collaborators, leading to a scalability chal
Externí odkaz:
http://arxiv.org/abs/2403.04968
Unsupervised representation learning has seen tremendous progress but is constrained by its reliance on data modality-specific stationarity and topology, a limitation not found in biological intelligence systems. For instance, human vision processes
Externí odkaz:
http://arxiv.org/abs/2310.04496
Co-occurrence statistics based word embedding techniques have proved to be very useful in extracting the semantic and syntactic representation of words as low dimensional continuous vectors. In this work, we discovered that dictionary learning can op
Externí odkaz:
http://arxiv.org/abs/1910.03833