1 citations · 1 across the 7 of their papers we have counts for
9 papers
SpatialAvatar-0: High-Quality 4D Head Avatar with Multi-Stage Reconstruction
Yiran Wang, Zeyu Zhang, Yuanming Li +2
High-quality 4D head avatars from one or a few source portraits are central to telepresence, AR/VR, and digital-human interaction. 3D Gaussian Splatting (3DGS) has emerged as the d…
Explicit Representation Alignment for Multimodal Sentiment Analysis
Baode Wang, Ziming Wang, Huacan Wang +2
Multimodal affective analysis aims to understand human sentiment and emotion by jointly modeling heterogeneous modalities such as text and images. However, multimodal models often…
AdaSpark: Adaptive Sparsity for Efficient Long-Video Understanding
Handong Li, Zikang Liu, Longteng Guo +10
Processing long-form videos with Video Large Language Models (Video-LLMs) is computationally prohibitive. Current efficiency methods often compromise fine-grained perception throug…
ViT: Unlocking Test-Time Training in Vision
Dongchen Han, Yining Li, Tianyu Li +6
Test-Time Training (TTT) has recently emerged as a promising direction for efficient sequence modeling. TTT reformulates attention operation as an online learning problem, construc…
Mobile-R1: Towards Interactive Capability for VLM-Based Mobile Agent via Systematic Training
Jihao Gu, Qihang Ai, Yingyao Wang +10
Vision-language model-based mobile agents have gained the ability to understand complex instructions and mobile screenshots, benefiting from reinforcement learning paradigms like G…
CombatVLA: An Efficient Vision-Language-Action Model for Combat Tasks in 3D Action Role-Playing Games
Peng Chen, Pi Bu, Yingyao Wang +9
Recent advances in Vision-Language-Action models (VLAs) have expanded the capabilities of embodied intelligence. However, significant challenges remain in real-time decision-making…
"See the World, Discover Knowledge": A Chinese Factuality Evaluation for Large Vision Language Models
Jihao Gu, Yingyao Wang, Pi Bu +17
The evaluation of factual accuracy in large vision language models (LVLMs) has lagged behind their rapid development, making it challenging to fully reflect these models' knowledge…
Global Compression Commander: Plug-and-Play Inference Acceleration for High-Resolution Large Vision-Language Models
Xuyang Liu, Ziming Wang, Junjie Chen +6
Large vision-language models (LVLMs) excel at visual understanding, but face efficiency challenges due to quadratic complexity in processing long multi-modal contexts. While token…
ConvLLaVA: Hierarchical Backbones as Visual Encoder for Large Multimodal Models
Chunjiang Ge, Sijie Cheng, Ziming Wang +6
High-resolution Large Multimodal Models (LMMs) encounter the challenges of excessive visual tokens and quadratic visual complexity. Current high-resolution LMMs address the quadrat…