7 papers · 1 filter
Seeing Without Exposing: Adaptive Privacy Control for Open-World, Context-Hungry MLLMs
Siyuan Xu, Yibing Liu, Peilin Chen +3
Multimodal large language models (MLLMs) have raised new privacy challenges. On the data side, user-provided inputs often include unpredictable sensitive information; while on the…
When Recovery Matters: The Blind Spot of Surrogate Privacy in MLLM Editing
Siyuan Xu, Yibing Liu, Peilin Chen +3
Multimodal Large Language Models (MLLMs) enable flexible instruction-driven image editing, but privacy risks arise when user images expose diverse and user-specific private content…
Joint Semantic and Rendering Enhancements in 3D Gaussian Modeling with Anisotropic Local Encoding
Jingming He, Chongyi Li, Shiqi Wang +1
Recent works propose extending 3DGS with semantic feature vectors for simultaneous semantic segmentation and image rendering. However, these methods often treat the semantic and re…
When Privacy Meets Recovery: The Overlooked Half of Surrogate-Driven Privacy Preservation for MLLM Editing
Siyuan Xu, Yibing Liu, Peilin Chen +3
Privacy leakage in Multimodal Large Language Models (MLLMs) has long been an intractable problem. Existing studies, though effectively obscure private information in MLLMs, often o…
When Video Coding Meets Multimodal Large Language Models: A Unified Paradigm for Video Coding
Pingping Zhang, Jinlong Li, Kecheng Chen +6
Existing codecs are designed to eliminate intrinsic redundancies to create a compact representation for compression. However, strong external priors from Multimodal Large Language…
AI-generated Image Quality Assessment in Visual Communication
Yu Tian, Yixuan Li, Baoliang Chen +3
Assessing the quality of artificial intelligence-generated images (AIGIs) plays a crucial role in their application in real-world scenarios. However, traditional image quality asse…