2 citations · 4 across the 6 of their papers we have counts for
5 papers · 1 filter
LEO-VL: Efficient Scene Representation for Scalable 3D Vision-Language Learning
Jiangyong Huang, Xiaojian Ma, Xiongkun Linghu +6
Developing vision-language models (VLMs) capable of understanding 3D scenes has been a longstanding research goal. Despite recent progress, 3D VLMs still struggle with spatial reas…
Embodied VideoAgent: Persistent Memory from Egocentric Videos and Embodied Sensors Enables Dynamic Scene Understanding
Yue Fan, Xiaojian Ma, Rongpeng Su +4
This paper investigates the problem of understanding dynamic 3D scenes from egocentric observations, a key challenge in robotics and embodied AI. Unlike prior studies that explored…
LongViTU: Instruction Tuning for Long-Form Video Understanding
Rujie Wu, Xiaojian Ma, Hai Ci +5
This paper introduces LongViTU, a large-scale (~121k QA pairs, ~900h videos), automatically generated dataset for long-form video understanding. We propose a systematic approach th…
Semantic Gaussians: Open-Vocabulary Scene Understanding with 3D Gaussian Splatting
Jun Guo, Xiaojian Ma, Yue Fan +2
Open-vocabulary 3D scene understanding presents a significant challenge in computer vision, with wide-ranging applications in embodied agents and augmented reality systems. Existin…
VideoAgent: A Memory-augmented Multimodal Agent for Video Understanding
Yue Fan, Xiaojian Ma, Rujie Wu +4
We explore how reconciling several foundation models (large language models and vision-language models) with a novel unified memory mechanism could tackle the challenging video und…