7 citations · 12 across the 7 of their papers we have counts for
7 papers
Exploring Spatial Intelligence from a Generative Perspective
Muzhi Zhu, Shunyao Jiang, Huanyi Zheng +9
Spatial intelligence is essential for multimodal large language models, yet current benchmarks largely assess it only from an understanding perspective. We ask whether modern gener…
RealHD: A High-Quality Dataset for Robust Detection of State-of-the-Art AI-Generated Images
Hanzhe Yu, Yun Ye, Jintao Rong +2
The rapid advancement of generative AI has raised concerns about the authenticity of digital images, as highly realistic fake images can now be generated at low cost, potentially i…
When Distillation Breaks Motion Control: Restoring Generative Trajectories for Fast Video Generators
Jintao Rong, Xin Xie, Xinyi Yu +4
Training-free motion customization imposes motion patterns from reference videos onto video generators through test-time computation. Most existing methods target full diffusion mo…
GSORB-SLAM: Gaussian Splatting SLAM benefits from ORB features and Transmittance information
Wancai Zheng, Xinyi Yu, Jintao Rong +3
The emergence of 3D Gaussian Splatting (3DGS) has recently ignited a renewed wave of research in dense visual SLAM. However, existing approaches encounter challenges, including sen…
Improving Neural Indoor Surface Reconstruction with Mask-Guided Adaptive Consistency Constraints
Xinyi Yu, Liqin Lu, Jintao Rong +2
3D scene reconstruction from 2D images has been a long-standing task. Instead of estimating per-frame depth maps and fusing them in 3D, recent research leverages the neural implici…
Retrieval-Enhanced Visual Prompt Learning for Few-shot Classification
Jintao Rong, Hao Chen, Linlin Ou +3
The Contrastive Language-Image Pretraining (CLIP) model has been widely used in various downstream vision tasks. The few-shot learning paradigm has been widely adopted to augment i…
Efficient Re-parameterization Operations Search for Easy-to-Deploy Network Based on Directional Evolutionary Strategy
Xinyi Yu, Xiaowei Wang, Jintao Rong +2
Structural re-parameterization (Rep) methods has achieved significant performance improvement on traditional convolutional network. Most current Rep methods rely on prior knowledge…