DSH-Bench is a benchmark for subject-driven T2I generation that uses hierarchical taxonomy sampling, difficulty/scenario classification, and a new SICS metric showing 9.4% higher human correlation than prior measures.
In: 2009 IEEE conference on computer vision and pattern recognition
2 Pith papers cite this work. Polarity classification is still indexing.
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Introduces Hybrid Tuning adapter with frequency filtering and noise estimation to adapt CLIP for ultrasound segmentation and classification, claiming outperformance on six multi-center datasets.
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DSH-Bench: A Difficulty- and Scenario-Aware Benchmark with Hierarchical Subject Taxonomy for Subject-Driven Text-to-Image Generation
DSH-Bench is a benchmark for subject-driven T2I generation that uses hierarchical taxonomy sampling, difficulty/scenario classification, and a new SICS metric showing 9.4% higher human correlation than prior measures.
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Adapting Vision-Language Foundation Model for Next Generation Medical Ultrasound Image Analysis
Introduces Hybrid Tuning adapter with frequency filtering and noise estimation to adapt CLIP for ultrasound segmentation and classification, claiming outperformance on six multi-center datasets.