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Found 9 papers, 1 papers with code
Learning with User-Level Local Differential Privacy
Under the central model, user-level DP is strictly stronger than the item-level one.
Distinguish Any Fake Videos: Unleashing the Power of Large-scale Data and Motion Features
Current datasets lack a varied and comprehensive repository of real and generated content for effective discrimination.
Enhancing Learning with Label Differential Privacy by Vector Approximation
Label differential privacy (DP) is a framework that protects the privacy of labels in training datasets, while the feature vectors are public.
CG-FedLLM: How to Compress Gradients in Federated Fune-tuning for Large Language Models
The success of current Large-Language Models (LLMs) hinges on extensive training data that is collected and stored centrally, called Centralized Learning (CL).
A Huber Loss Minimization Approach to Mean Estimation under User-level Differential Privacy
We provide a theoretical analysis of our approach, which gives the noise strength needed for privacy protection, as well as the bound of mean squared error.
Learn Suspected Anomalies from Event Prompts for Video Anomaly Detection
Most models for weakly supervised video anomaly detection (WS-VAD) rely on multiple instance learning, aiming to distinguish normal and abnormal snippets without specifying the type of anomaly.
Geometric-Aware Low-Light Image and Video Enhancement via Depth Guidance
First, a depth-aware feature extraction module is designed to inject depth priors into the image representation.
Video Frame Interpolation with Region-Distinguishable Priors from SAM
In existing Video Frame Interpolation (VFI) approaches, the motion estimation between neighboring frames plays a crucial role.
Dynamic Relevance Learning for Few-Shot Object Detection
By adjusting the prediction distribution of the base detector using the output of this GCN, the proposed model serves as a hard auxiliary classification task, which guides the detector to improve the class representation implicitly.
