5 citations · 15 across the 6 of their papers we have counts for
6 papers
Explicitly Increasing Input Information Density for Vision Transformers on Small Datasets
Xiangyu Chen, Ying Qin, Wenju Xu +3
Vision Transformers have attracted a lot of attention recently since the successful implementation of Vision Transformer (ViT) on vision tasks. With vision Transformers, specifical…
Accumulated Trivial Attention Matters in Vision Transformers on Small Datasets
Xiangyu Chen, Qinghao Hu, Kaidong Li +2
Vision Transformers has demonstrated competitive performance on computer vision tasks benefiting from their ability to capture long-range dependencies with multi-head self-attentio…
Dilated Continuous Random Field for Semantic Segmentation
Xi Mo, Xiangyu Chen, Cuncong Zhong +3
Mean field approximation methodology has laid the foundation of modern Continuous Random Field (CRF) based solutions for the refinement of semantic segmentation. In this paper, we…
Realtime Global Attention Network for Semantic Segmentation
Xi Mo, Xiangyu Chen
In this paper, we proposed an end-to-end realtime global attention neural network (RGANet) for the challenging task of semantic segmentation. Different from the encoding strategy d…
Audio-Visual Transformer Based Crowd Counting
Usman Sajid, Xiangyu Chen, Hasan Sajid +2
Crowd estimation is a very challenging problem. The most recent study tries to exploit auditory information to aid the visual models, however, the performance is limited due to the…
Few-Shot Learning by Integrating Spatial and Frequency Representation
Xiangyu Chen, Guanghui Wang
Human beings can recognize new objects with only a few labeled examples, however, few-shot learning remains a challenging problem for machine learning systems. Most previous algori…