Can SAM Count Anything? An Empirical Study on SAM Counting

04/21/2023
by   Zhiheng Ma, et al.
0

Meta AI recently released the Segment Anything model (SAM), which has garnered attention due to its impressive performance in class-agnostic segmenting. In this study, we explore the use of SAM for the challenging task of few-shot object counting, which involves counting objects of an unseen category by providing a few bounding boxes of examples. We compare SAM's performance with other few-shot counting methods and find that it is currently unsatisfactory without further fine-tuning, particularly for small and crowded objects. Code can be found at <https://github.com/Vision-Intelligence-and-Robots-Group/count-anything>.

READ FULL TEXT
research
04/16/2021

Learning To Count Everything

Existing works on visual counting primarily focus on one specific catego...
research
03/03/2023

Zero-shot Object Counting

Class-agnostic object counting aims to count object instances of an arbi...
research
12/11/2021

Object Counting: You Only Need to Look at One

This paper aims to tackle the challenging task of one-shot object counti...
research
01/19/2018

Detecting and counting tiny faces

Finding Tiny Faces by Hu and Ramanan - and released at CVPR 2017 - propo...
research
04/10/2023

Can SAM Segment Anything? When SAM Meets Camouflaged Object Detection

SAM is a segmentation model recently released by Meta AI Research and ha...
research
08/09/2023

Advancing Early Detection of Virus Yellows: Developing a Hybrid Convolutional Neural Network for Automatic Aphid Counting in Sugar Beet Fields

Aphids are efficient vectors to transmit virus yellows in sugar beet fie...
research
06/17/2020

Overcoming Statistical Shortcuts for Open-ended Visual Counting

Machine learning models tend to over-rely on statistical shortcuts. Thes...

Please sign up or login with your details

Forgot password? Click here to reset