Hierarchical Walking Transformer for Object Re-Identification
Xudong Tian, Jun Liu, Zhizhong Zhang, Chengjie Wang, Yanyun Qu, Yuan Xie, Lizhuang Ma
2022Year
6Citations
Abstract
Recently, transformer purely based on attention mechanism has been applied to a wide range of tasks and achieved impressive performance. Though extensive efforts have been made, there are still drawbacks to the transformer architecture which hinder its further applications: (i) the quadratic complexity brought by attention mechanism; (ii) barely incorporated inductive bias.
Ask about this paper
Ask your agent about it.
Lune has read the top-tier papers around this one, so every answer names the papers it rests on.
Your agent calls
Lunesearch_papers
Free to start. No credit card required.
Terminal
Install the CLIlune papers get bccd9a87-ead2-41c7-a1f8-913cec1fa697Related papers
- Chunk, Align, Select: A Simple Long-sequence Processing Method for TransformersJiawen Xie, Pengyu Cheng, Xiao Liang, Yong Dai et al.ACL 2024
- Flowformer: Linearizing Transformers with Conservation FlowsHaixu Wu, Jialong Wu, Jiehui Xu, Jianmin Wang et al.ICML 2022 · 130 citations
- The Unstoppable Rise of Computational Linguistics in Deep LearningJames HendersonACL 2020 · 4 citations
- Mega: Moving Average Equipped Gated AttentionXuezhe Ma, Chunting Zhou, Xiang Kong, Junxian He et al.ICLR 2023 · 36 citations
- T-former: An Efficient Transformer for Image InpaintingYe Deng, Siqi Hui, Sanping Zhou, Deyu Meng et al.ACM MM 2022 · 58 citations
