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Found 6 papers, 4 papers with code
TomatoDIFF: On-plant Tomato Segmentation with Denoising Diffusion Models
Artificial intelligence applications enable farmers to optimize crop growth and production while reducing costs and environmental impact.
Synthetic Data for Face Recognition: Current State and Future Prospects
Over the past years, deep learning capabilities and the availability of large-scale training datasets advanced rapidly, leading to breakthroughs in face recognition accuracy.
SeeABLE: Soft Discrepancies and Bounded Contrastive Learning for Exposing Deepfakes
Modern deepfake detectors have achieved encouraging results, when training and test images are drawn from the same data collection.
Fairness in Face Presentation Attack Detection
Face recognition (FR) algorithms have been proven to exhibit discriminatory behaviors against certain demographic and non-demographic groups, raising ethical and legal concerns regarding their deployment in real-world scenarios.
UG^2: a Video Benchmark for Assessing the Impact of Image Restoration and Enhancement on Automatic Visual Recognition
Advances in image restoration and enhancement techniques have led to discussion about how such algorithmscan be applied as a pre-processing step to improve automatic visual recognition.
To Frontalize or Not To Frontalize: Do We Really Need Elaborate Pre-processing To Improve Face Recognition?
If a CNN is intended to tolerate facial pose, then we face an important question: should this training data be diverse in its pose distribution, or should face images be normalized to a single pose in a pre-processing step?
