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20182025
most citedDomain Enhanced Arbitrary Image Style Transfer via Contrastive Learning

209 citations · 299 across the 24 of their papers we have counts for

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Showing 2022Show all

5 papers · 1 filter

cs.CV2022★ 1 cited

Inversion-Based Style Transfer with Diffusion Models

Yuxin Zhang, Nisha Huang, Fan Tang +4

The artistic style within a painting is the means of expression, which includes not only the painting material, colors, and brushstrokes, but also the high-level attributes includi…

cs.CV2022★ 5 cited

DiffStyler: Controllable Dual Diffusion for Text-Driven Image Stylization

Nisha Huang, Yuxin Zhang, Fan Tang +5

Despite the impressive results of arbitrary image-guided style transfer methods, text-driven image stylization has recently been proposed for transferring a natural image into a st…

cs.CV2022★ 1 cited

Draw Your Art Dream: Diverse Digital Art Synthesis with Multimodal Guided Diffusion

Nisha Huang, Fan Tang, Weiming Dong +1

Digital art synthesis is receiving increasing attention in the multimedia community because of engaging the public with art effectively. Current digital art synthesis methods usual…

cs.CV2022★ 209 cited

Domain Enhanced Arbitrary Image Style Transfer via Contrastive Learning

Yuxin Zhang, Fan Tang, Weiming Dong +4

In this work, we tackle the challenging problem of arbitrary image style transfer using a novel style feature representation learning method. A suitable style representation, as a…

cs.CV2022★ 2 cited

CrossRectify: Leveraging Disagreement for Semi-supervised Object Detection

Chengcheng Ma, Xingjia Pan, Qixiang Ye +3

Semi-supervised object detection has recently achieved substantial progress. As a mainstream solution, the self-labeling-based methods train the detector on both labeled data and u…