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Found 6 papers, 4 papers with code
Harm Amplification in Text-to-Image Models
Text-to-image (T2I) models have emerged as a significant advancement in generative AI; however, there exist safety concerns regarding their potential to produce harmful image outputs even when users input seemingly safe prompts.
An Improved Lightweight YOLOv5 Model Based on Attention Mechanism for Face Mask Detection
However, existing deep learning based models struggle to simultaneously achieve the requirements of both high precision and real-time performance.
How to Synthesize a Large-Scale and Trainable Micro-Expression Dataset?
To this end, we develop a protocol to automatically synthesize large scale MiE training data that allow us to train improved recognition models for real-world test data.
Synthetic Data Are as Good as the Real for Association Knowledge Learning in Multi-object Tracking
We show that compared with real data, association knowledge obtained from synthetic data can achieve very similar performance on real-world test sets without domain adaption techniques.
Boosting Semi-Supervised Face Recognition with Noise Robustness
This paper presents an effective solution to semi-supervised face recognition that is robust to the label noise aroused by the auto-labelling.
Semi-Siamese Training for Shallow Face Learning
Extensive experiments on various benchmarks of face recognition show the proposed method significantly improves the training, not only in shallow face learning, but also for conventional deep face data.
