.png){kind=icon}
返回
Cross-View Representation Learning: A Superior ContextIB Method for Logo Classification
.png){kind=icon}
PRE
AI
DOI：10.1109/LSP.2024.3356419.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Logo classification systems have become increasingly important in various industries for tasks, such as infringement detection and industrial production. However, challenges still exist in logo classification due to real-world image background interference, the high similarity between classes, labeling difficulties, and the insufficient representation of occlusion in single-view logos. Many existing algorithms fail to consider the data characteristics and the intrinsic information of multiple views, which limits their performance. To overcome these limitations, we developed a novel Cross-View Information Awareness Network (CVIA-Net) for logo classification. To differentiate between similar logo categories, the CVIA-Net novel learns context-shared features of the same category via a self-supervised way without labeled, which solves the problem of insufficient features due to occlusion. For single-view images, CVIA-Net establishes a bottleneck representation to address background interference. Extensive experiments on three datasets demonstrate that it outperforms state-of-the-art methods. The method is expected to advance the development of cross-view representation learning.
Keyword:
Feature extraction
Representation learning
Image coding
Data mining
Task analysis
Interference
Classification algorithms
Artificial intelligence
computer vision
informat- ion bottleneck
logo classification
representation learning
期刊
IF:
9.6
论文数:
1.1W
被引数:
1.7W
机构
引用论文
.png){kind=icon}
Inflexibility of mental planning: A characteristic disorder with prefrontal lobe lesions?心理计划的僵化: 前额叶病变的特征性障碍？
PREAI
分享收藏
Microstructure of Low-fat Cheddar Cheese Containing Varying Concentrations of Sucrose Polyesters
PREAI
分享收藏
查看更多内容