Leveraging Cross-Modal Neighbor Representation for Improved CLIP Classification

Yi, Chao; Ren, Lu; Zhan, De-Chuan; Ye, Han-Jia

doi:10.1109/cvpr52733.2024.02587

2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2024

DOI: 10.1109/cvpr52733.2024.02587

|View full text |Cite

Leveraging Cross-Modal Neighbor Representation for Improved CLIP Classification

Chao Yi,

Lu Ren,

De-Chuan Zhan

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Dynamic Convolution Neural Networks with Both Global and Local Attention for Image Classification

Zheng,

Li,

et al. 2024

Mathematics

View full text Add to dashboard Cite

Convolution is a crucial component of convolution neural networks (CNNs). However, the standard static convolution has two primary defects: data independence and the weak ability to integrate global and local features. This paper proposes a novel and efficient dynamic convolution method with global and local attention to address these issues. A building block called the Global and Local Attention Unit (GLAU) is designed, in which a weighted fusion of global channel attention kernels and local spatial attention kernels generates the proposed dynamic convolution kernels. The GLAU is data-dependent and has better adaptability and the ability to integrate global and local features into each layer. We refer to such modified CNNs with GLAUs as “GLAUNets”. Extensive evaluation experiments for image classification compared to classical CNNs and the state-of-the-art dynamic convolution neural networks were conducted on the popular benchmark datasets. In terms of classification accuracy, the number of parameters, and computational complexity, the experimental results demonstrate the outstanding performance of GLAUNets.

show abstract

Dynamic Convolution Neural Networks with Both Global and Local Attention for Image Classification

Zheng,

Li,

et al. 2024

Mathematics

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Leveraging Cross-Modal Neighbor Representation for Improved CLIP Classification

Cited by 1 publication

References 16 publications

Dynamic Convolution Neural Networks with Both Global and Local Attention for Image Classification

Dynamic Convolution Neural Networks with Both Global and Local Attention for Image Classification

Contact Info

Product

Resources

About