5 citations · 10 across the 4 of their papers we have counts for
4 papers
Cambrian-S: Towards Spatial Supersensing in Video
Shusheng Yang, Jihan Yang, Pinzhi Huang +12
We argue that progress in true multimodal intelligence calls for a shift from reactive, task-driven systems and brute-force long context towards a broader paradigm of supersensing.…
MindCube: Spatial Mental Modeling from Limited Views
Qineng Wang, Baiqiao Yin, Pingyue Zhang +11
Can Vision-Language Models (VLMs) imagine the full scene from just a few views, like humans do? Humans form spatial mental models naturally, internal representations of unseen spac…
Thinking in Space: How Multimodal Large Language Models See, Remember, and Recall Spaces
Jihan Yang, Shusheng Yang, Anjali W. Gupta +3
Humans possess the visual-spatial intelligence to remember spaces from sequential visual observations. However, can Multimodal Large Language Models (MLLMs) trained on million-scal…
Leveraging Large (Visual) Language Models for Robot 3D Scene Understanding
William Chen, Siyi Hu, Rajat Talak +1
Abstract semantic 3D scene understanding is a problem of critical importance in robotics. As robots still lack the common-sense knowledge about household objects and locations of a…