8 papers
VSE-MOT: Multi-Object Tracking in Low-Quality Video Scenes Guided by Visual Semantic Enhancement
Jun Du, Weiwei Xing, Ming Li +1
Current multi-object tracking (MOT) algorithms typically overlook issues inherent in low-quality videos, leading to significant degradation in tracking performance when confronted…
Safe-Sora: Safe Text-to-Video Generation via Graphical Watermarking
Zihan Su, Xuerui Qiu, Hongbin Xu +6
The explosive growth of generative video models has amplified the demand for reliable copyright preservation of AI-generated content. Despite its popularity in image synthesis, inv…
Synthetic-to-Real Self-supervised Robust Depth Estimation via Learning with Motion and Structure Priors
Weilong Yan, Ming Li, Haipeng Li +2
Self-supervised depth estimation from monocular cameras in diverse outdoor conditions, such as daytime, rain, and nighttime, is challenging due to the difficulty of learning univer…
Safe-VAR: Safe Visual Autoregressive Model for Text-to-Image Generative Watermarking
Ziyi Wang, Songbai Tan, Gang Xu +5
With the success of autoregressive learning in large language models, it has become a dominant approach for text-to-image generation, offering high efficiency and visual quality. H…
PVChat: Personalized Video Chat with One-Shot Learning
Yufei Shi, Weilong Yan, Gang Xu +6
Video large language models (ViLLMs) excel in general video understanding, e.g., recognizing activities like talking and eating, but struggle with identity-aware comprehension, suc…
X-SGS: Safe and Generalizable Gaussian Splatting with X-dimensional Watermarks
Zihang Cheng, Wentao Bao, Huiping Zhuang +6
3D Gaussian Splatting (3DGS) has been widely used in 3D reconstruction and 3D generation. However, the rapid adoption of 3D Gaussian Splatting raises growing concerns about informa…
EventGPT: Event Stream Understanding with Multimodal Large Language Models
Shaoyu Liu, Jianing Li, Guanghui Zhao +5
Event cameras record visual information as asynchronous pixel change streams, excelling at scene perception under unsatisfactory lighting or high-dynamic conditions. Existing multi…
Uncertainty Quantification via Hölder Divergence for Multi-View Representation Learning
Yan Zhang, Ming Li, Chun Li +3
Evidence-based deep learning represents a burgeoning paradigm for uncertainty estimation, offering reliable predictions with negligible extra computational overheads. Existing meth…