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Sizhou Ma

2 accepted papers

2024

DSL-FIQA: Assessing Facial Image Quality via Dual-Set Degradation Learning and Landmark-Guided Transformer

CVPR 2024poster

Generic Face Image Quality Assessment (GFIQA) evaluates the perceptual quality of facial images which is crucial in improving image restoration algorithms and selecting high-quality face images for downstream tasks. We present a novel transformer-based method for GFIQA which is aided by two unique m…

Cited by 9SourcePDFScholar
2024

RobustSAM: Segment Anything Robustly on Degraded Images

CVPR 2024highlight

Segment Anything Model (SAM) has emerged as a transformative approach in image segmentation acclaimed for its robust zero-shot segmentation capabilities and flexible prompting system. Nonetheless its performance is challenged by images with degraded quality. Addressing this limitation we propose the…