Vision Transformer
Vision Transformers (ViTs) adapt the transformer architecture, initially designed for natural language processing, to image analysis by treating images as sequences of patches. Current research focuses on improving ViT efficiency and robustness through techniques like token pruning, attention engineering, and hybrid models combining ViTs with convolutional neural networks or other architectures (e.g., Mamba). These advancements are driving progress in various applications, including medical image analysis, object detection, and spatiotemporal prediction, by offering improved accuracy and efficiency compared to traditional convolutional neural networks in specific tasks.
Papers
Deep Learning for Active Region Classification: A Systematic Study from Convolutional Neural Networks to Vision Transformers
Edoardo Legnaro, Sabrina Guastavino, Michele Piana, Anna Maria Massone
Is Smoothness the Key to Robustness? A Comparison of Attention and Convolution Models Using a Novel Metric
Baiyuan Chen
ED-ViT: Splitting Vision Transformer for Distributed Inference on Edge Devices
Xiang Liu, Yijun Song, Xia Li, Yifei Sun, Huiying Lan, Zemin Liu, Linshan Jiang, Jialin Li
CTA-Net: A CNN-Transformer Aggregation Network for Improving Multi-Scale Feature Extraction
Chunlei Meng, Jiacheng Yang, Wei Lin, Bowen Liu, Hongda Zhang, chun ouyang, Zhongxue Gan
NARAIM: Native Aspect Ratio Autoregressive Image Models
Daniel Gallo Fernández, Robert van der Klis, Răzvan-Andrei Matişan, Janusz Partyka, Efstratios Gavves, Samuele Papa, Phillip Lippe
STA-Unet: Rethink the semantic redundant for Medical Imaging Segmentation
Vamsi Krishna Vasa, Wenhui Zhu, Xiwen Chen, Peijie Qiu, Xuanzhao Dong, Yalin Wang
Towards virtual painting recolouring using Vision Transformer on X-Ray Fluorescence datacubes
Alessandro Bombini, Fernando García-Avello Bofías, Francesca Giambi, Chiara Ruberto
DeBiFormer: Vision Transformer with Deformable Agent Bi-level Routing Attention
Nguyen Huu Bao Long, Chenyu Zhang, Yuzhi Shi, Tsubasa Hirakawa, Takayoshi Yamashita, Tohgoroh Matsui, Hironobu Fujiyoshi
HorGait: A Hybrid Model for Accurate Gait Recognition in LiDAR Point Cloud Planar Projections
Jiaxing Hao, Yanxi Wang, Zhigang Chang, Hongmin Gao, Zihao Cheng, Chen Wu, Xin Zhao, Peiye Fang, Rachmat Muwardi