790 citations · 2.7k across the 36 of their papers we have counts for
4 papers · 1 filter
Boosting Few-shot Semantic Segmentation with Transformers
Guolei Sun, Yun Liu, Jingyun Liang +1
Due to the fact that fully supervised semantic segmentation methods require sufficient fully-labeled data to work well and can not generalize to unseen classes, few-shot segmentati…
Boosting Salient Object Detection with Transformer-based Asymmetric Bilateral U-Net
Yu Qiu, Yun Liu, Le Zhang +1
Existing salient object detection (SOD) methods mainly rely on U-shaped convolution neural networks (CNNs) with skip connections to combine the global contexts and local spatial de…
P2T: Pyramid Pooling Transformer for Scene Understanding
Yu-Huan Wu, Yun Liu, Xin Zhan +1
Recently, the vision transformer has achieved great success by pushing the state-of-the-art of various vision tasks. One of the most challenging problems in the vision transformer…
Rethinking Global Context in Crowd Counting
Guolei Sun, Yun Liu, Thomas Probst +3
This paper investigates the role of global context for crowd counting. Specifically, a pure transformer is used to extract features with global information from overlapping image p…