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cs.CV2026
Steering Diffusion Models via Class-Contrastive Influence for Few-Shot Medical Classification
Jeeyung Kim, Erfan Esmaeili, Qiang Qiu
When labeled data are scarce, off-the-shelf diffusion models can augment training sets for few-shot medical image classification, but not all generated samples are equally useful f…
cs.CV2024
Text Embedding is Not All You Need: Attention Control for Text-to-Image Semantic Alignment with Text Self-Attention Maps
Jeeyung Kim, Erfan Esmaeili, Qiang Qiu
In text-to-image diffusion models, the cross-attention map of each text token indicates the specific image regions attended. Comparing these maps of syntactically related tokens pr…
cs.CV2024
Model-Agnostic Human Preference Inversion in Diffusion Models
Jeeyung Kim, Ze Wang, Qiang Qiu
Efficient text-to-image generation remains a challenging task due to the high computational costs associated with the multi-step sampling in diffusion models. Although distillation…