1 citations · 1 across the 12 of their papers we have counts for
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When Safety Collides: Resolving Multi-Category Harmful Conflicts in Text-to-Image Diffusion via Adaptive Safety Guidance
Yongli Xiang, Ziming Hong, Zhaoqing Wang +3
Text-to-Image (T2I) diffusion models have demonstrated significant advancements in generating high-quality images, while raising potential safety concerns regarding harmful content…
SenTSR-Bench: Thinking with Injected Knowledge for Time-Series Reasoning
Zelin He, Boran Han, Xiyuan Zhang +10
Time-series diagnostic reasoning is essential for many applications, yet existing solutions face a persistent gap: general reasoning large language models (GRLMs) possess strong re…
MeGU: Machine-Guided Unlearning with Target Feature Disentanglement
Haoyu Wang, Zhuo Huang, Xiaolong Wang +3
The growing concern over training data privacy has elevated the "Right to be Forgotten" into a critical requirement, thereby raising the demand for effective Machine Unlearning. Ho…
ALIVE: Animate Your World with Lifelike Audio-Video Generation
Ying Guo, Qijun Gan, Yifu Zhang +13
Video generation is rapidly evolving towards unified audio-video generation. In this paper, we present ALIVE, a generation model that adapts a pretrained Text-to-Video (T2V) model…
Per-parameter Task Arithmetic for Unlearning in Large Language Models
Chengyi Cai, Zesheng Ye, Jiangchao Yao +5
In large language model (LLM) unlearning, private information is required to be removed. Task arithmetic unlearns by subtracting a specific task vector (TV)--defined as the paramet…
Visual-Guided Key-Token Regularization for Multimodal Large Language Model Unlearning
Chengyi Cai, Zesheng Ye, Peike Li +3
Unlearning in Multimodal Large Language Models (MLLMs) prevents the model from revealing private information when queried about target images. Existing MLLM unlearning methods larg…