29 citations · 37 across the 8 of their papers we have counts for
8 papers
Xiaomi-Robotics-U0: Unified Embodied Synthesis with World Foundation Model
Xinghang Li, Jun Guo, Qiwei Li +21
Recent foundation image and video generation models offer strong generalization and controllability, but their direct application to embodied scenarios is limited by requirements f…
SpatialVAM:Spatial-Aware Multi-View Video Diffusion as a Data-Efficient Robot Policy
Peiyan Li, Yixiang Chen, Yuan Xu +13
Robotic manipulation requires understanding both the 3D spatial structure of the environment and its temporal evolution, yet most existing policies neglect one or both aspects. The…
What Matters in Building Vision-Language-Action Models for Generalist Robots
Xinghang Li, Peiyan Li, Long Qian +10
To utilize Foundation Vision Language Models (VLMs) for robotic tasks and motion planning, the community has proposed different methods for injecting action components into VLMs an…
STEP: Enhancing Video-LLMs' Compositional Reasoning by Spatio-Temporal Graph-guided Self-Training
Haiyi Qiu, Minghe Gao, Long Qian +7
Video Large Language Models (Video-LLMs) have recently shown strong performance in basic video understanding tasks, such as captioning and coarse-grained question answering, but st…
Momentor: Advancing Video Large Language Model with Fine-Grained Temporal Reasoning
Long Qian, Juncheng Li, Yu Wu +5
Large Language Models (LLMs) demonstrate remarkable proficiency in comprehending and handling text-based tasks. Many efforts are being made to transfer these attributes to video mo…
Fine-Grained Semantically Aligned Vision-Language Pre-Training
Juncheng Li, Xin He, Longhui Wei +6
Large-scale vision-language pre-training has shown impressive advances in a wide range of downstream tasks. Existing methods mainly model the cross-modal alignment by the similarit…
Dilated Context Integrated Network with Cross-Modal Consensus for Temporal Emotion Localization in Videos
Juncheng Li, Junlin Xie, Linchao Zhu +8
Understanding human emotions is a crucial ability for intelligent robots to provide better human-robot interactions. The existing works are limited to trimmed video-level emotion c…
Compositional Temporal Grounding with Structured Variational Cross-Graph Correspondence Learning
Juncheng Li, Junlin Xie, Long Qian +6
Temporal grounding in videos aims to localize one target video segment that semantically corresponds to a given query sentence. Thanks to the semantic diversity of natural language…