Paper1:《Respecting Transfer Gap in Knowledge Distillation》From Damo Alibaba.OpenReviewHighlight文章Introduction的故事讲得比较好,属于由一个小的实验统计现象,编一个小的故事引出motivation的写法。 文章说的是… 阅读全文 《Sparse R-CNN: End-to-End Object Detection with Learnable Proposals》笔记 ...
Code for CVPR 2022 Oral paper: 'Few-Shot Object Detection with Fully Cross-Transformer' - GuangxingHan/FCT
Few-Shot Object Detection(FSOD)是计算机视觉中一个快速发展的领域。它包括查找给定类集的所有出现,每个类只有几个带注释的示例。已经提出了许多方法来应对这一挑战,其中大多数是基于注意力机制的。然而,种类繁多的经典目标检测框架和训练策略使得方法之间的性能比较变得困难。
本文的few-shot detection setting base classes:Cb中每个类别包括大量instance novel classes:Cn中每个类别仅包含K个instance(通常少于10个),k-shot 本文 synthetic datasets:COCO & VOC,其中用于训练的novel set里每个class的object数量相等(也就是K-shot) natural datasets:LVIS,具有natural long-tail distribution。
In this paper, we tackle a challenging problem of Few-shot Object Detection rather than recognition. We propose Power Normalizing Second-order Detector consisting of the Encoding Network (EN), the Multi-scale Feature Fusion (MFF), Second-order Pooling (SOP) with Power Normalization (PN), the ...
目标检测算法(two-stage)的典型代表是R-CNN,很多few-shot object detection算法基于R-CNN架构进一步探索,目前已经取得不错进展的方法包含:single-stage methods 和 anchor-free methods. 3. Few-Shot Object Detection(小样本目标检测) 对于Few-shot目标检测算法的探索主要分成两个方向:meta-learning(元学习) 和 tran...
论文阅读笔记《Few-Shot Object Detection with Attention-RPN and Multi-Relation Detector》,程序员大本营,技术文章内容聚合第一站。
Although it has great potential and has achieved many achievements, it cannot be denied that object detection faces many challenges such as the small size and the quality of training datasets. The few-shot paradigm was explored to tackle that challenge. In this paper, we intensively investigate ...
86 papers with code • 9 benchmarks • 7 datasets 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
result, it combines different relevant clues in order to enhance the discrimination power of the learned detectors and reduce class confusion. We evaluate the proposed method on two challenging few-shot object detection benchmarks, and empirical results show that the proposed method obtains competitive...