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20092023
most citedLearning with Structured Sparsity

293 citations · 1.2k across the 76 of their papers we have counts for

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Showing 2022Show all

21 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.CV2022★ 22 cited

Visual Prompt Tuning for Test-time Domain Adaptation

Yunhe Gao, Xingjian Shi, Yi Zhu +5

Models should be able to adapt to unseen data during test-time to avoid performance drops caused by inevitable distribution shifts in real-world deployment scenarios. In this work,…

cs.CV2022★ 2 cited

CDDSA: Contrastive Domain Disentanglement and Style Augmentation for Generalizable Medical Image Segmentation

Ran Gu, Guotai Wang, Jiangshan Lu +8

Generalization to previously unseen images with potential domain shifts and different styles is essential for clinically applicable medical image segmentation, and the ability to d…

cs.CV2022★ 1 cited

Automatic Tooth Segmentation from 3D Dental Model using Deep Learning: A Quantitative Analysis of what can be learnt from a Single 3D Dental Model

Ananya Jana, Hrebesh Molly Subhash, Dimitris Metaxas

3D tooth segmentation is an important task for digital orthodontics. Several Deep Learning methods have been proposed for automatic tooth segmentation from 3D dental models or intr…

cs.CV2022★ 2 cited

Exploiting Unlabeled Data with Vision and Language Models for Object Detection

Shiyu Zhao, Zhixing Zhang, Samuel Schulter +5

Building robust and generic object detection frameworks requires scaling to larger label spaces and bigger training datasets. However, it is prohibitively costly to acquire annotat…