most citedLGM-Net: Learning to Generate Matching Networks for Few-Shot Learning

50 citations · 86 across the 8 of their papers we have counts for

collaborators

8 papers

cs.CV202014 cited

Dynamic Refinement Network for Oriented and Densely Packed Object Detection

Xingjia Pan, Yuqiang Ren, Kekai Sheng +5

Object detection has achieved remarkable progress in the past decade. However, the detection of oriented and densely packed objects remains challenging because of following inheren…

cs.CV20203 cited

Distribution Aligned Multimodal and Multi-Domain Image Stylization

Minxuan Lin, Fan Tang, Weiming Dong +3

Multimodal and multi-domain stylization are two important problems in the field of image style transfer. Currently, there are few methods that can perform both multimodal and multi…

cs.CV202010 cited

Arbitrary Style Transfer via Multi-Adaptation Network

Yingying Deng, Fan Tang, Weiming Dong +3

Arbitrary style transfer is a significant topic with research value and application prospect. A desired style transfer, given a content image and referenced style painting, would r…

cs.CV2020

Multi-Attribute Guided Painting Generation

Minxuan Lin, Yingying Deng, Fan Tang +2

Controllable painting generation plays a pivotal role in image stylization. Currently, the control way of style transfer is subject to exemplar-based reference or a random one-hot…

cs.CV20197 cited

Revisiting Image Aesthetic Assessment via Self-Supervised Feature Learning

Kekai Sheng, Weiming Dong, Menglei Chai +6

Visual aesthetic assessment has been an active research field for decades. Although latest methods have achieved promising performance on benchmark datasets, they typically rely on…

cs.LG20192 cited

Incremental Concept Learning via Online Generative Memory Recall

Huaiyu Li, Weiming Dong, Bao-Gang Hu

The ability to learn more and more concepts over time from incrementally arriving data is essential for the development of a life-long learning system. However, deep neural network…