Core Concepts
UniHDAは、複数のモーダルを持つハイブリッドドメイン適応のための統一された多目的フレームワークです。
Abstract
Directory:
Introduction
Generative Domain Adaptation Progress
Limitations of Existing Methods
Methodology
Multi-Modal Hybrid Domain Adaptation Approach
Experiments
Experimental Setting and Datasets Used
Image-Image, Text-Text, and Image-Text Hybrid Domain Adaptation Results
Comparison with Existing Methods
Efficiency Comparison with NADA, MTG, DiFa, DE, and FHDA
Generalization on 3D Generator and Diffusion Model
Ablation Studies on CSS Loss and Encoder Impact
Conclusion & Limitations
Stats
"Experiments show that the adapted generator can synthesize realistic images with various attribute compositions."
"UniHDA is agnostic to the type of generators, enabling broader application across various models."
Quotes
"UniHDA maintains strong consistency and effectively generates images with characteristics of the hybrid domain."
"UniHDA well captures the attributes of the hybrid target domain and maintains strong cross-domain consistency."