1 citations · 2 across the 9 of their papers we have counts for
9 papers
SphereVideo: Prototype-anchored Hyperspherical Boundary for Continual AI-generated Video Detection
Fei Li, Yue Yu, Yuran Wang +3
AI-generated video (AIGV) detection aims to distinguish real videos from AI-generated ones. In practice, detectors trained on existing data often fail to generalize to newly emergi…
DECODE: Tackling Representation and Decision Degradation in Continual AI-Generated Image Detection
Zihao Cai, Xinghan Li, Ruiyan Yang +3
As generative models continue to evolve, AI-generated image detectors must incrementally adapt to emerging generative domains while preserving knowledge acquired from previous ones…
VIGIL: Part-Grounded Structured Reasoning for Generalizable Deepfake Detection
Xinghan Li, Junhao Xu, Jingjing Chen
Multimodal large language models (MLLMs) offer a promising path toward interpretable deepfake detection by generating textual explanations. However, the reasoning process of curren…
Adam Reduces a Unique Form of Sharpness: Theoretical Insights Near the Minimizer Manifold
Xinghan Li, Haodong Wen, Kaifeng Lyu
Despite the popularity of the Adam optimizer in practice, most theoretical analyses study Stochastic Gradient Descent (SGD) as a proxy for Adam, and little is known about how the s…
Emu3.5: Native Multimodal Models are World Learners
Yufeng Cui, Honghao Chen, Haoge Deng +20
We introduce Emu3.5, a large-scale multimodal world model that natively predicts the next state across vision and language. Emu3.5 is pre-trained end-to-end with a unified next-tok…
From Spatial to Actions: Grounding Vision-Language-Action Model in Spatial Foundation Priors
Zhengshen Zhang, Hao Li, Yalun Dai +10
Existing vision-language-action (VLA) models act in 3D real-world but are typically built on 2D encoders, leaving a spatial reasoning gap that limits generalization and adaptabilit…
Unified Vision-Language-Action Model
Yuqi Wang, Xinghang Li, Wenxuan Wang +5
Vision-language-action models (VLAs) have garnered significant attention for their potential in advancing robotic manipulation. However, previous approaches predominantly rely on t…
SafeGenBench: A Benchmark Framework for Security Vulnerability Detection in LLM-Generated Code
Xinghang Li, Jingzhe Ding, Chao Peng +4
The code generation capabilities of large language models(LLMs) have emerged as a critical dimension in evaluating their overall performance. However, prior research has largely ov…
Revealing the Implicit Noise-based Imprint of Generative Models
Xinghan Li, Yue Yu, Xue Song +2
With the rapid advancement of vision generation models, the potential security risks stemming from synthetic visual content have garnered increasing attention, posing significant c…