Abstract: We tackle a new task of few-shot object counting and detection. Given a few exemplar bounding boxes of a target object class, we seek to count and detect all objects of the target class. This task shares the same supervision as the few-shot object counting but additionally outputs...
This work studies the problem offew-shot object counting, which counts the number of exemplar objects (i.e., described by one or several support images) occurring in the query image.The major challenge lies in that the target objects can be densely packed in the query image, making it hard...
Scale-Prior Deformable Convolution for Exemplar-Guided Class-Agnostic Counting CounTR: Transformer-based Generalised Visual Counting Few-shot Object Counting with Similarity-Aware Feature Enhancement 2023 CAN SAM COUNT ANYTHING? AN EMPIRICAL STUDY ON SAM COUNTING Zero-Shot Object Counting 2021 Learning To ...
Few-shot counting 是一种相对新颖的研究方向，其目标是在少量标记样本的情况下，实现对目标物体的准确计...
Few-Shot Object Counting and DetectionECCV 2022FSCD-147 & FSCD-LVISPDFCODE Mutually Reinforcing Structure with Proposal Contrastive Consistency for Few-Shot Object DetectionECCV 2022PASCAL VOC & MS COCOPDFCODE Few-Shot End-to-End Object Detection via Constantly Concentrated Encoding across HeadsECCV ...
This work studies the problem of few-shot object counting, which counts the number of exemplar objects (i.e., described by one or several support images) occurring in the query image. The major challenge lies in that the target objects can be densely packed in the query image, making it ...
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 ...
Hallucination ImprovesFew-ShotObject Detection 1、摘要 学习从少量的注释实例中检测新目标具有重要的现实意义。当例子极其有限(少于三个)时,就会出现一种特别具有挑战性而又普遍的制度。改进少样本检测的一个关键因素是解决缺乏变化的训练数据。我们提出通过从基类转移共享的类内变异来为新类建立一个更好的变异模型。为...
目标函数:使用自适应高斯核生成gt,MSE作为loss函数; 实验结果 b:结果提升主要在于Norm 样本数量对结果的影响 在其他特定计数数据集上的泛化性能 总结:对于不同尺寸的support样本特征,在进行计算相似度特征图的时候,大尺寸的support特征会导致相似度图的值整体较高,Norm的使用解决了这一问题。
This work studies the problem of few-shot object counting, which counts the number of exemplar objects (i.e., described by one or several support images) occurring in the query image. The major challenge lies in that the target objects can be densely packed in the query image, making it ...