3 citations · 3 across the 7 of their papers we have counts for
4 papers · 1 filter
Universal Few-Shot Spatial Control for Diffusion Models
Kiet T. Nguyen, Chanhyuk Lee, Donggyun Kim +2
Spatial conditioning in pretrained text-to-image diffusion models has significantly improved fine-grained control over the structure of generated images. However, existing control…
Chameleon: A Data-Efficient Generalist for Dense Visual Prediction in the Wild
Donggyun Kim, Seongwoong Cho, Semin Kim +2
Large language models have evolved data-efficient generalists, benefiting from the universal language interface and large-scale pre-training. However, constructing a data-efficient…
Universal Few-shot Learning of Dense Prediction Tasks with Visual Token Matching
Donggyun Kim, Jinwoo Kim, Seongwoong Cho +2
Dense prediction tasks are a fundamental class of problems in computer vision. As supervised methods suffer from high pixel-wise labeling cost, a few-shot learning solution that ca…
High-Fidelity Synthesis with Disentangled Representation
Wonkwang Lee, Donggyun Kim, Seunghoon Hong +1
Learning disentangled representation of data without supervision is an important step towards improving the interpretability of generative models. Despite recent advances in disent…