27 citations · 127 across the 18 of their papers we have counts for
6 papers · 1 filter
Shunted Self-Attention via Multi-Scale Token Aggregation
Sucheng Ren, Daquan Zhou, Shengfeng He +2
Recent Vision Transformer~(ViT) models have demonstrated encouraging results across various computer vision tasks, thanks to their competence in modeling long-range dependencies of…
From Continuity to Editability: Inverting GANs with Consecutive Images
Yangyang Xu, Yong Du, Wenpeng Xiao +2
Existing GAN inversion methods are stuck in a paradox that the inverted codes can either achieve high-fidelity reconstruction, or retain the editing capability. Having only one of…
Reducing Spatial Labeling Redundancy for Semi-supervised Crowd Counting
Yongtuo Liu, Sucheng Ren, Liangyu Chai +4
Labeling is onerous for crowd counting as it should annotate each individual in crowd images. Recently, several methods have been proposed for semi-supervised crowd counting to red…
Fine-grained Domain Adaptive Crowd Counting via Point-derived Segmentation
Yongtuo Liu, Dan Xu, Sucheng Ren +3
Due to domain shift, a large performance drop is usually observed when a trained crowd counting model is deployed in the wild. While existing domain-adaptive crowd counting methods…
Unifying Global-Local Representations in Salient Object Detection with Transformer
Sucheng Ren, Qiang Wen, Nanxuan Zhao +2
The fully convolutional network (FCN) has dominated salient object detection for a long period. However, the locality of CNN requires the model deep enough to have a global recepti…
Co-advise: Cross Inductive Bias Distillation
Sucheng Ren, Zhengqi Gao, Tianyu Hua +4
Transformers recently are adapted from the community of natural language processing as a promising substitute of convolution-based neural networks for visual learning tasks. Howeve…