2 citations · 3 across the 11 of their papers we have counts for
12 papers
Grounded Action Model: 3D Grounding as a Foundation for Robotics
Gehao Zhang, Weikai Huang, Shailesh Shailesh +3
Manipulation policies must know which objects matter and where they are, yet the pretrained backbones that current robot foundation models build on, from language in vision-languag…
Imaginative Perception Tokens Enhance Spatial Reasoning in Multimodal Language Models
Mahtab Bigverdi, Linjie Li, Weikai Huang +8
Vision language models (VLMs) excel at many tasks but still struggle with spatial reasoning when critical information is not directly observable. Many such problems require imagina…
MolmoAct2: Action Reasoning Models for Real-world Deployment
Haoquan Fang, Jiafei Duan, Donovan Clay +26
Vision-Language-Action (VLA) models aim to provide a single generalist controller for robots, but today's systems fall short on the criteria that matter for real-world deployment.…
WildDet3D: Scaling Promptable 3D Detection in the Wild
Weikai Huang, Jieyu Zhang, Sijun Li +15
Understanding objects in 3D from a single image is a cornerstone of spatial intelligence. A key step toward this goal is monocular 3D object detection--recovering the extent, locat…
Synthetic Visual Genome 2: Extracting Large-scale Spatio-Temporal Scene Graphs from Videos
Ziqi Gao, Jieyu Zhang, Wisdom Oluchi Ikezogwo +10
We introduce Synthetic Visual Genome 2 (SVG2), a large-scale panoptic video scene graph dataset. SVG2 contains over 636K videos with 6.6M objects, 52.0M attributes, and 6.7M relati…
TrajTok: Learning Trajectory Tokens enables better Video Understanding
Chenhao Zheng, Jieyu Zhang, Jianing Zhang +6
Tokenization in video models, typically through patchification, generates an excessive and redundant number of tokens. This severely limits video efficiency and scalability. While…
Molmo2: Open Weights and Data for Vision-Language Models with Video Understanding and Grounding
Christopher Clark, Jieyu Zhang, Zixian Ma +18
Today's strongest video-language models (VLMs) remain proprietary. The strongest open-weight models either rely on synthetic data from proprietary VLMs, effectively distilling from…
Synthetic Object Compositions for Scalable and Accurate Learning in Detection, Segmentation, and Grounding
Weikai Huang, Jieyu Zhang, Taoyang Jia +5
Visual grouping -- operationalized through tasks such as instance segmentation, visual grounding, and object detection -- enables applications ranging from robotic perception to ph…
ProVision: Programmatically Scaling Vision-centric Instruction Data for Multimodal Language Models
Jieyu Zhang, Le Xue, Linxin Song +11
With the rise of multimodal applications, instruction data has become critical for training multimodal language models capable of understanding complex image-based queries. Existin…
Generate Any Scene: Scene Graph Driven Data Synthesis for Visual Generation Training
Ziqi Gao, Weikai Huang, Jieyu Zhang +2
Recent advances in text-to-vision generation excel in visual fidelity but struggle with compositional generalization and semantic alignment. Existing datasets are noisy and weakly…
Task Me Anything
Jieyu Zhang, Weikai Huang, Zixian Ma +7
Benchmarks for large multimodal language models (MLMs) now serve to simultaneously assess the general capabilities of models instead of evaluating for a specific capability. As a r…
m&m's: A Benchmark to Evaluate Tool-Use for multi-step multi-modal Tasks
Zixian Ma, Weikai Huang, Jieyu Zhang +2
Real-world multi-modal problems are rarely solved by a single machine learning model, and often require multi-step computational plans that involve stitching several models. Tool-a…