假定Support set中有N个类,每个类K个样本(N-way K-shot),模型对每个类的K个样本分别提取特征向量,然后对这K个向量取平均,对这N个类执行相同操作,就可得到NxK维的矩阵。用此矩阵与第1步提取的特征向量做channelwise multiplication,便可得到新的权重,用这个权重作为第3步的初始权重进行预测。 3、预测 采用第...
solution: few-shot learning, learning to learn transferable knowledge that can be well generalized to new classes and therefore performs image recognition on new classes with only a few annotated examples limitations: designed for common objects in optical images where objects are generally of consisten...
Few-Shot Object Detection on Remote Sensing Images via Shared Attention Module and Balanced Fine-Tuning Strategy Few-shot object detection is a recently emerging branch in the field of computer vision. Recent research studies have proposed several effective methods for object detection with few samples...
Currently, extensive research has been conducted on few-shot object detection in natural scene datasets, and notable progress has been made. However, in the realm of remote sensing, this technology is still lagging behind. Furthermore, many established methods rely on two-stage detectors, ...
few-shot object detection; remote sensing images; meta-learning; incompletely annotated objects1. Introduction In recent decades, advancements in remote sensing technology have led to the development of spaceborne sensors, which now offer sub-meter spatial resolution, comparable to airborne images from ...
Applications of Few-Shot Learning Few-Shot Learning has been used extensively in several fields in the Deep Learning literature, from Computer Vision tasks like image classification and object detection to Remote Sensing, Natural Language Processing, etc. Let us briefly discuss them in this section....
To this end, we explore recent methods and ideas from open-vocabulary detection for the remote sensing domain. We develop a few-shot object detector based on a traditional two-stage architecture, where the classification block is replaced by a prototype-based classifier. A large-scale pre-...
- 《International Journal of Remote Sensing》 被引量: 0发表: 2023年 Stability Plasticity Decoupled Fine-tuning For Few-shot end-to-end Object Detection Few-shot object detection(FSOD) aims to design methods to adapt object detectors efficiently with only few annotated samples. Fine-tuning has ...
In addition, this paper provides some discussions on open challenges that few/zero-shot learning brought to visual semantic segmentation, such as cross-domain few/zero-shot segmentation and generalized few/zero-shot segmentation. In summary, the main contributions of this paper are as follows: 1)...
To further enhance CLIP’s adaption capability, existing methods proposed to fine-tune additional learnable modules, which significantly improves the few-shot performance but introduces extra training time and computational resources. In this paper, we propose a Training-free adaption method for CLIP to...