Hybrid Multi-Granularity Approach for Few-Shot Image Retrieval with Weak Features
This paper proposes a multi-granularity retrieval algorithm based on an unsupervised image augmentation network. The algorithm designs a feature extraction method (AugODNet_BRA) rooted in image augmentation, which efficiently captures high-level semantic features of images with few samples, small ta...
Saved in:
| Main Authors: | Aiguo Lu, Zican Li, Yanwei Liu, Pandi Liu, Ke Wang |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
|
| Series: | Algorithms |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1999-4893/18/6/329 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
YOLOv8-GO: A Lightweight Model for Prompt Detection of Foliar Maize Diseases
by: Tianyue Jiang, et al.
Published: (2024-11-01) -
Small object detection algorithm based on improved YOLOv10 for traffic sign
by: Yukang Zou, et al.
Published: (2025-07-01) -
Velocity Profile Geometries and Granular Temperature Distributions in Very Dense Granular Flows
by: Yan Li, et al.
Published: (2024-01-01) -
Multi-Granularity Domain-Adaptive Teacher for Unsupervised Remote Sensing Object Detection
by: Fang Fang, et al.
Published: (2025-05-01) -
An Omni-Dimensional Dynamic Convolutional Network for Single-Image Super-Resolution Tasks
by: Xi Chen, et al.
Published: (2025-07-01)