Year: 2025
Type: conference-paper
Abstract: daptation (SODA), which balances computational efficiency and representation capacity. Extensive evaluations on text-to-image diffusion models demonstrate SODA's effectiveness, offering a spectrum-aware alternative to existing fine-tuning methods.
Authors: Xinxi Zhang, Song Wen, Ligong Han, Felix Juefei-Xu, Akash Srivastava, Junzhou Huang, Vladimir Pavlovic, Hao Wang, Molei Tao, Dimitris Metaxas
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: SCC-IRG Track 2: Making Micromobility Smarter and Safer (M2S2), Collaborative Research: HCC: Medium: Linguistically-Driven Sign Recognition from Continuous Signing for American Sign Language (ASL), G3278210685, SCH: Counterfactual Explanations for AI-Assisted Cancer Diagnosis and Subtypiing, NSF Convergence Accelerator Track H: AI-based Tools to Enhance Access and Opportunities for the Deaf, G6377968432, Machine Learning and Deformable Model-based 4D Characterization of Cardiac Dyssynchrony from MRI, G7718032457, Center: IUCRC Phase II Rutgers University: Center for Accelerated and Real Time Analytics (CARTA), CDS&E: Collaborative Research: Private Data Analytics Synthesis, and Sharing for Large-Scale Multi-Modal Smart City Mobility Research
OpenAlex ID: W4409261979