13 citations · 76 across the 20 of their papers we have counts for
7 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…
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…
On Feature Decorrelation in Self-Supervised Learning
Tianyu Hua, Wenxiao Wang, Zihui Xue +3
In self-supervised representation learning, a common idea behind most of the state-of-the-art approaches is to enforce the robustness of the representations to predefined augmentat…
Multimodal Knowledge Expansion
Zihui Xue, Sucheng Ren, Zhengqi Gao +1
The popularity of multimodal sensors and the accessibility of the Internet have brought us a massive amount of unlabeled multimodal data. Since existing datasets and well-trained m…