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Found 7 papers, 3 papers with code
V2X-Real: a Largs-Scale Dataset for Vehicle-to-Everything Cooperative Perception
Recent advancements in Vehicle-to-Everything (V2X) technologies have enabled autonomous vehicles to share sensing information to see through occlusions, greatly boosting the perception capability.
Zero-Shot Digital Rock Image Segmentation with a Fine-Tuned Segment Anything Model
Despite its advanced features, SAM struggles with rock CT/SEM images due to their absence in its training set and the low-contrast nature of grayscale images.
Enhancing Rock Image Segmentation in Digital Rock Physics: A Fusion of Generative AI and State-of-the-Art Neural Networks
In digital rock physics, analysing microstructures from CT and SEM scans is crucial for estimating properties like porosity and pore connectivity.
DED: Diagnostic Evidence Distillation for acne severity grading on face images
In this study, we propose an acne diagnosis method, Diagnostic Evidence Distillation (DED), that suitably adapts the characteristics of acne diagnosis and can be applied to diagnose under different acne criteria.
Ranked #1 on
Acne Severity Grading
on ACNE04
(Accuracy metric)
Acne Severity Grading on Face Images via Extraction and Guidance of Prior Knowledge
Acne Vulgaris seriously affects people’s daily life.
Ranked #2 on
Acne Severity Grading
on ACNE04
(Accuracy metric)
KIEGLFN: A unified acne grading framework on face images
Methods: A unified acne grading framework that can be generalized to apply referring to different grading criteria is developed.
Ranked #3 on
Acne Severity Grading
on ACNE04
(Accuracy metric)
An Acne Grading Framework on Face Images via Skin Attention and SFNet
In this paper, the global estimation of acne severity grading is studied by Convolutional Neural Networks (CNNs) and a unified acne grading framework that can diagnose referring to different grading criteria is proposed.
Ranked #3 on
Acne Severity Grading
on ACNE04
(Accuracy metric)
