主要和两个基线模型对比:One-shot Instance Segmentation 和 Class-agnostic Counting。 One-shot Instance Segmentation实验设置:该模型用于单样本语义分割,手工设置box置信度阈值 ([0.70, 0.99]); Class-agnostic Counting:该模型首先在ImageNet预训练然后在target数据集上fintune,与本文模型只在COCO数据集上预训练在tar...
包含SCM和FEM两大部分。 SCM SCM可以生成更可靠的相似度图。具体做法如下: 可学的特征映射:在fs和fQ之后 经过一层共享的1*1卷积和LN层; 特征对比:用fs的特征作为算子在fQ上卷积,生成R0; 归一化:包括样本归一化和空间一化;样本归一化如下:相当于是在support样本数量维度上的norm; 样本归一化 空间归一化: 空间...
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 ...
Official PyTorch Implementation ofFew-shot Object Counting with Similarity-Aware Feature Enhancement, Accepted by WACV 2023. 1. Quick Start 1.1 FSC147 in Original Setting Create the FSC147 dataset directory. Download the FSC147 dataset fromhere. Unzip the file and move some to./data/FSC147_384_...
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...
总的来说，Few-shot counting 这个方向的发展还处于起步阶段，尚未形成完整的研究体系和成熟的技术方案。
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 ...
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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 ...