activity
20242026
collaborators

9 papers

cs.CL2026

Training-free Truthfulness Detection via Sparse MLP Value Vectors

Runheng Liu, Heyan Huang, Xingchen Xiao +2

Large language models (LLMs) are prone to generating factually incorrect content, motivating methods for assessing truthfulness from internal model signals. While supervised probin…

cs.CV2026

Do Protective Perturbations Really Protect Portrait Privacy under Real-world Image Transformations?

Ruiqing Sun, Xingshan Yao, Zhijing Wu +6

Proactive defense methods protect portrait images from unauthorized editing or talking face generation (TFG) by introducing pixel-level protective perturbations, and have attracted…

cs.CV2026

ISExplore:Informative Segment Selection for Efficient Personalized 3D Talking Face Generation

Rui-Qing Sun, Ang Li, Zhijing Wu +5

Talking Face Generation (TFG) methods based on Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) have recently achieved impressive progress in personalized talking hea…

cs.AI2026

DeepSurvey-Bench: Evaluating Academic Value of Automatically Generated Scientific Surveys

Guo-Biao Zhang, Ding-Yuan Liu, Da-Yi Wu +5

The rapid development of automated survey generation technology has made it increasingly important to establish a comprehensive benchmark to evaluate the quality of generated surve…

cs.CV2026

Efficient and Robust Video Defense Framework against 3D-field Personalized Talking Face

Rui-qing Sun, Xingshan Yao, Tian Lan +6

State-of-the-art 3D-field video-referenced Talking Face Generation (TFG) methods synthesize high-fidelity personalized talking-face videos in real time by modeling 3D geometry and…

cs.CL2025

A Survey of Automatic Evaluation Methods on Text, Visual and Speech Generations

Tian Lan, Yang-Hao Zhou, Zi-Ao Ma +8

Recent advances in deep learning have significantly enhanced generative AI capabilities across text, images, and audio. However, automatically evaluating the quality of these gener…