5 papers
From Foundation to Application: Improving VLA Models in Practice
Wei Wu, Fangjing Wang, Fan Lu +21
Despite recent progress of VLA foundation models, the disparity between laboratory conditions and real-world applications continues to impede their practical implementation. To bri…
GVC-Seg: Training-Free 3D Instance Segmentation via Geometric Visual Correspondence
Liang Xu, Fangjing Wang, Jinyu Yang +1
Accurate 3D instance segmentation in point cloud data is critical for machine vision applications. Recent advancements leverage multiple pre-trained foundation models to generate 3…
ST4VLA: Spatially Guided Training for Vision-Language-Action Models
Jinhui Ye, Fangjing Wang, Ning Gao +9
Large vision-language models (VLMs) excel at multimodal understanding but fall short when extended to embodied tasks, where instructions must be transformed into low-level motor ac…
InternVLA-M1: A Spatially Guided Vision-Language-Action Framework for Generalist Robot Policy
Xinyi Chen, Yilun Chen, Yanwei Fu +26
We introduce InternVLA-M1, a unified framework for spatial grounding and robot control that advances instruction-following robots toward scalable, general-purpose intelligence. Its…
HCNQA: Enhancing 3D VQA with Hierarchical Concentration Narrowing Supervision
Shengli Zhou, Jianuo Zhu, Qilin Huang +3
3D Visual Question-Answering (3D VQA) is pivotal for models to perceive the physical world and perform spatial reasoning. Answer-centric supervision is a commonly used training met…