We aim to tackle the challenging Few-Shot Object Detec- tion (FSOD), where data-scarce categories are presented during the model learning.
Abstract: We aim to tackle the challenging Few-Shot Object Detection (FSOD), where data-scarce categories are presented during the model learning.
We aim to tackle the challenging Few-Shot Object Detec- tion (FSOD), where data-scarce categories are presented during the model learning.
We aim to tackle the challenging Few-Shot Object Detection (FSOD) where data-scarce categories are presented during the model learning.
People also ask
What is object detection and classification?
What is small object detection and classification?
What is classification loss in object detection?
What is the difference between pixel classification and object detection?
A paper list of Few-shot Object Detection. Contribute to piddnad/few-shot-object-detection-papers development by creating an account on GitHub.
Collect some papers about few-shot object detection for computer vision. Additionally, we briefly introduce the commonly used datasets for few-shot object�...
Apr 7, 2024 � This paper presents a comprehensive survey to review the significant advancements in the field of FSOD in recent years and summarize the existing challenges�...
Few-shot object detec- tion via classification refinement and distractor retreatment. ... Multi-scale positive sample refinement for few-shot object detection.
Nov 9, 2021 � The paper proposes a novel approach for few-shot object detection by first assigning a novel (few-shot) class to a base category (Association)�...
Few-Shot Object Detection is a computer vision task that involves detecting objects in images with limited training data. The goal is to train a model on a�...