activity
20172024
most citedUnsupervised Domain Adaptation via Calibrating Uncertainties

17 citations · 83 across the 22 of their papers we have counts for

collaborators
Showing 2022Show all

5 papers · 1 filter

cs.CV2022★ 12 cited

Diffusion Guided Domain Adaptation of Image Generators

Kunpeng Song, Ligong Han, Bingchen Liu +2

Can a text-to-image diffusion model be used as a training objective for adapting a GAN generator to another domain? In this paper, we show that the classifier-free guidance can be…

cs.CV2022★ 6 cited

SINE: SINgle Image Editing with Text-to-Image Diffusion Models

Zhixing Zhang, Ligong Han, Arnab Ghosh +2

Recent works on diffusion models have demonstrated a strong capability for conditioning image generation, e.g., text-guided image synthesis. Such success inspires many efforts tryi…

cs.LG2022

On the Importance of Calibration in Semi-supervised Learning

Charlotte Loh, Rumen Dangovski, Shivchander Sudalairaj +5

State-of-the-art (SOTA) semi-supervised learning (SSL) methods have been highly successful in leveraging a mix of labeled and unlabeled data by combining techniques of consistency…

cs.CV2022

Hierarchically Self-Supervised Transformer for Human Skeleton Representation Learning

Yuxiao Chen, Long Zhao, Jianbo Yuan +5

Despite the success of fully-supervised human skeleton sequence modeling, utilizing self-supervised pre-training for skeleton sequence representation learning has been an active fi…

cs.CV2022

Show Me What and Tell Me How: Video Synthesis via Multimodal Conditioning

Ligong Han, Jian Ren, Hsin-Ying Lee +5

Most methods for conditional video synthesis use a single modality as the condition. This comes with major limitations. For example, it is problematic for a model conditioned on an…