4 citations · 5 across the 27 of their papers we have counts for
11 papers · 1 filter
Topology-Agnostic Animal Motion Generation from Text Prompt
Keyi Chen, Mingze Sun, Zhenyu Liu +2
Motion generation is fundamental to computer animation and widely used across entertainment, robotics, and virtual environments. While recent methods achieve impressive results, mo…
Reasoning-VLA: A Fast and General Vision-Language-Action Reasoning Model for Autonomous Driving
Dapeng Zhang, Zhenlong Yuan, Zhangquan Chen +5
Vision-Language-Action (VLA) models have recently shown strong decision-making capabilities in autonomous driving. However, existing VLAs often struggle with achieving efficient in…
VisMem: Latent Vision Memory Unlocks Potential of Vision-Language Models
Xinlei Yu, Chengming Xu, Guibin Zhang +7
Despite the remarkable success of Vision-Language Models (VLMs), their performance on a range of complex visual tasks is often hindered by a "visual processing bottleneck": a prope…
Think with 3D: Geometric Imagination Grounded Spatial Reasoning from Limited Views
Zhangquan Chen, Manyuan Zhang, Xinlei Yu +8
Though recent advances in vision-language models (VLMs) have achieved remarkable progress across a wide range of multimodal tasks, understanding 3D spatial relationships from limit…
LLaVA-OneVision-1.5: Fully Open Framework for Democratized Multimodal Training
Xiang An, Yin Xie, Kaicheng Yang +20
We present LLaVA-OneVision-1.5, a novel family of Large Multimodal Models (LMMs) that achieve state-of-the-art performance with significantly reduced computational and financial co…
Visual Multi-Agent System: Mitigating Hallucination Snowballing via Visual Flow
Xinlei Yu, Chengming Xu, Guibin Zhang +8
Multi-Agent System (MAS) powered by Visual Language Models (VLMs) enables challenging tasks but suffers from a novel failure term, multi-agent visual hallucination snowballing, whe…
SIFThinker: Spatially-Aware Image Focus for Visual Reasoning
Zhangquan Chen, Ruihui Zhao, Chuwei Luo +4
Current multimodal large language models (MLLMs) still face significant challenges in complex visual tasks (e.g., spatial understanding, fine-grained perception). Prior methods hav…
Visual Document Understanding and Reasoning: A Multi-Agent Collaboration Framework with Agent-Wise Adaptive Test-Time Scaling
Xinlei Yu, Chengming Xu, Zhangquan Chen +6
The dominant paradigm of monolithic scaling in Vision-Language Models (VLMs) is failing for understanding and reasoning in documents, yielding diminishing returns as it struggles w…
NFR: Neural Feature-Guided Non-Rigid Shape Registration
Zhangquan Chen, Puhua Jiang, Mingze Sun +1
In this paper, we propose a novel learning-based framework for 3D shape registration, which overcomes the challenges of significant non-rigid deformation and partiality undergoing…
Adversarial Robustness for Unified Multi-Modal Encoders via Efficient Calibration
Chih-Ting Liao, Zhangquan Chen, Chunlei Meng +3
Recent unified multi-modal encoders align a wide range of modalities into a shared representation space, enabling diverse cross-modal tasks. Despite their impressive capabilities,…
VisRL: Intention-Driven Visual Perception via Reinforced Reasoning
Zhangquan Chen, Xufang Luo, Dongsheng Li
Visual understanding is inherently intention-driven - humans selectively focus on different regions of a scene based on their goals. Recent advances in large multimodal models (LMM…