2 citations · 7 across the 30 of their papers we have counts for
8 papers · 1 filter
SINGER: Vivid Audio-driven Singing Video Generation with Multi-scale Spectral Diffusion Model
Yan Li, Ziya Zhou, Zhiqiang Wang +3
Recent advancements in generative models have significantly enhanced talking face video generation, yet singing video generation remains underexplored. The differences between huma…
Foundation Cures Personalization: Improving Personalized Models' Prompt Consistency via Hidden Foundation Knowledge
Yiyang Cai, Zhengkai Jiang, Yulong Liu +4
Facial personalization faces challenges to maintain identity fidelity without disrupting the foundation model's prompt consistency. The mainstream personalization models employ ide…
You Know What I'm Saying: Jailbreak Attack via Implicit Reference
Tianyu Wu, Lingrui Mei, Ruibin Yuan +3
While recent advancements in large language model (LLM) alignment have enabled the effective identification of malicious objectives involving scene nesting and keyword rewriting, o…
EVA: An Embodied World Model for Future Video Anticipation
Xiaowei Chi, Chun-Kai Fan, Hengyuan Zhang +8
Video generation models have made significant progress in simulating future states, showcasing their potential as world simulators in embodied scenarios. However, existing models o…
Both Ears Wide Open: Towards Language-Driven Spatial Audio Generation
Peiwen Sun, Sitong Cheng, Xiangtai Li +5
Recently, diffusion models have achieved great success in mono-channel audio generation. However, when it comes to stereo audio generation, the soundscapes often have a complex sce…
HiPrompt: Tuning-free Higher-Resolution Generation with Hierarchical MLLM Prompts
Xinyu Liu, Yingqing He, Lanqing Guo +10
The potential for higher-resolution image generation using pretrained diffusion models is immense, yet these models often struggle with issues of object repetition and structural a…
AgentMonitor: A Plug-and-Play Framework for Predictive and Secure Multi-Agent Systems
Chi-Min Chan, Jianxuan Yu, Weize Chen +6
The rapid advancement of large language models (LLMs) has led to the rise of LLM-based agents. Recent research shows that multi-agent systems (MAS), where each agent plays a specif…
NoRA: Nested Low-Rank Adaptation for Efficient Fine-Tuning Large Models
Cheng Lin, Lujun Li, Dezhi Li +3
In this paper, we introduce Nested Low-Rank Adaptation (NoRA), a novel approach to parameter-efficient fine-tuning that extends the capabilities of Low-Rank Adaptation (LoRA) techn…