25 citations · 43 across the 12 of their papers we have counts for
12 papers
LaST-R1: Reinforcing Robotic Manipulation via Adaptive Physical Latent Reasoning
Hao Chen, Jiaming Liu, Zhonghao Yan +10
Robotic foundation models require reasoning over complex visual scenes to execute adaptive actions in dynamic environments. While recent studies on latent-reasoning Vision-Language…
LaST: Latent Spatio-Temporal Chain-of-Thought for Robotic Vision-Language-Action Model
Zhuoyang Liu, Jiaming Liu, Hao Chen +11
Vision-Language-Action (VLA) models have recently shown strong generalization, with some approaches seeking to explicitly generate linguistic reasoning traces or predict future obs…
IndusGCC: A Data Benchmark and Evaluation Framework for GUI-Based General Computer Control in Industrial Automation
Xiaoran Yang, Yuyang Du, Kexin Chen +14
As Industry 4.0 progresses, flexible manufacturing has become a cornerstone of modern industrial systems, with equipment automation playing a pivotal role. However, existing contro…
SciVerse: Unveiling the Knowledge Comprehension and Visual Reasoning of LMMs on Multi-modal Scientific Problems
Ziyu Guo, Ray Zhang, Hao Chen +4
The rapid advancement of Large Multi-modal Models (LMMs) has enabled their application in scientific problem-solving, yet their fine-grained capabilities remain under-explored. In…
HybridVLA: Collaborative Diffusion and Autoregression in a Unified Vision-Language-Action Model
Jiaming Liu, Hao Chen, Pengju An +12
A fundamental objective of manipulation policy design is to endow robots to comprehend human instructions, reason about scene cues, and execute generalized actions in dynamic envir…
MM-Mixing: Multi-Modal Mixing Alignment for 3D Understanding
Jiaze Wang, Yi Wang, Ziyu Guo +5
We introduce MM-Mixing, a multi-modal mixing alignment framework for 3D understanding. MM-Mixing applies mixing-based methods to multi-modal data, preserving and optimizing cross-m…
No Time to Train: Empowering Non-Parametric Networks for Few-shot 3D Scene Segmentation
Xiangyang Zhu, Renrui Zhang, Bowei He +6
To reduce the reliance on large-scale datasets, recent works in 3D segmentation resort to few-shot learning. Current 3D few-shot segmentation methods first pre-train models on 'see…
MathVerse: Does Your Multi-modal LLM Truly See the Diagrams in Visual Math Problems?
Renrui Zhang, Dongzhi Jiang, Yichi Zhang +8
The remarkable progress of Multi-modal Large Language Models (MLLMs) has garnered unparalleled attention, due to their superior performance in visual contexts. However, their capab…
LLM-Assisted Multi-Teacher Continual Learning for Visual Question Answering in Robotic Surgery
Yuyang Du, Kexin Chen, Yue Zhan +7
Visual question answering (VQA) is crucial for promoting surgical education. In practice, the needs of trainees are constantly evolving, such as learning more surgical types, adapt…
SignVTCL: Multi-Modal Continuous Sign Language Recognition Enhanced by Visual-Textual Contrastive Learning
Hao Chen, Jiaze Wang, Ziyu Guo +6
Sign language recognition (SLR) plays a vital role in facilitating communication for the hearing-impaired community. SLR is a weakly supervised task where entire videos are annotat…
ImageBind-LLM: Multi-modality Instruction Tuning
Jiaming Han, Renrui Zhang, Wenqi Shao +14
We present ImageBind-LLM, a multi-modality instruction tuning method of large language models (LLMs) via ImageBind. Existing works mainly focus on language and image instruction tu…
Point-Bind & Point-LLM: Aligning Point Cloud with Multi-modality for 3D Understanding, Generation, and Instruction Following
Ziyu Guo, Renrui Zhang, Xiangyang Zhu +8
We introduce Point-Bind, a 3D multi-modality model aligning point clouds with 2D image, language, audio, and video. Guided by ImageBind, we construct a joint embedding space betwee…