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20242026
most citedMulti-modal Retrieval Augmented Multi-modal Generation: Datasets, Evaluation Metrics and Strong Baselines

1 citations · 1 across the 8 of their papers we have counts for

collaborators

9 papers

cs.CV2026

Audio-Driven Adversarial Defense for 3D Talking Face Generation with totally Visual Fidelity Preservation

Rui-Qing Sun, Chen-Hao Cui, Hui-Yang Zhao +3

The rapid development of generative portrait models has raised growing concerns about privacy leakage and identity misuse. In particular, audio-driven 3D talking face generation ca…

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

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.CV2025

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.CL2025

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.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…

cs.AI2025

T2I-Eval-R1: Reinforcement Learning-Driven Reasoning for Interpretable Text-to-Image Evaluation

Zi-Ao Ma, Tian Lan, Rong-Cheng Tu +5

The rapid progress in diffusion-based text-to-image (T2I) generation has created an urgent need for interpretable automatic evaluation methods that can assess the quality of genera…

cs.IR2024

Subtopic-aware View Sampling and Temporal Aggregation for Long-form Document Matching

Youchao Zhou, Heyan Huang, Zhijing Wu +2

Long-form document matching aims to judge the relevance between two documents and has been applied to various scenarios. Most existing works utilize hierarchical or long context mo…

cs.CL2024★ 1 cited

Multi-modal Retrieval Augmented Multi-modal Generation: Datasets, Evaluation Metrics and Strong Baselines

Zi-Ao Ma, Tian Lan, Rong-Cheng Tu +6

We present a systematic investigation of Multi-modal Retrieval Augmented Multi-modal Generation (MRAG), a novel task that enables foundation models to process multi-modal web c…