# Object Counting: You Only Need to Look at One

arXiv: https://arxiv.org/abs/2112.05993 · HTML: https://mlsanity.com/paper/2112.05993/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 27 references that are in the map.
- First posted on arXiv 11 Dec 2021.

## Builds on (most cited first, 17 of 17)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Few-shot Object Detection via Feature Reweighting](https://mlsanity.com/paper/1812.01866/) (2018)
- [Few-Shot Object Detection with Attention-RPN and Multi-Relation Detector](https://mlsanity.com/paper/1908.01998/) (2019)
- [Bayesian Loss for Crowd Count Estimation with Point Supervision](https://mlsanity.com/paper/1908.03684/) (2019)
- [Context-Aware Crowd Counting](https://mlsanity.com/paper/1811.10452/) (2018)
- [Learning To Count Everything](https://mlsanity.com/paper/2104.08391/) (2021)
- [Distribution Matching for Crowd Counting](https://mlsanity.com/paper/2009.13077/) (2020)
- [Class-Agnostic Counting](https://mlsanity.com/paper/1811.00472/) (2018)
- [One-Shot Object Detection with Co-Attention and Co-Excitation](https://mlsanity.com/paper/1911.12529/) (2019)
- [One-Shot Instance Segmentation](https://mlsanity.com/paper/1811.11507/) (2018)
- [Counting Everyday Objects in Everyday Scenes](https://mlsanity.com/paper/1604.03505/) (2016)
- [Direct Measure Matching for Crowd Counting](https://mlsanity.com/paper/2107.01558/) (2021)
- [One-Shot Object Detection without Fine-Tuning](https://mlsanity.com/paper/2005.03819/) (2020)

## Built on by (most cited first, 13 of 13)

- [CLIP-Count: Towards Text-Guided Zero-Shot Object Counting](https://mlsanity.com/paper/2305.07304/) (2023)
- [A Low-Shot Object Counting Network With Iterative Prototype Adaptation](https://mlsanity.com/paper/2211.08217/) (2022)
- [Few-shot Object Counting and Detection](https://mlsanity.com/paper/2207.10988/) (2022)
- [Learning to Count Anything: Reference-less Class-agnostic Counting with Weak Supervision](https://mlsanity.com/paper/2205.10203/) (2022)
- [T-Rex: Counting by Visual Prompting](https://mlsanity.com/paper/2311.13596/) (2023)
- [Semi-supervised Crowd Counting via Density Agency](https://mlsanity.com/paper/2209.02955/) (2022)
- [Can SAM Count Anything? An Empirical Study on SAM Counting](https://mlsanity.com/paper/2304.10817/) (2023)
- [ABC Easy as 123: A Blind Counter for Exemplar-Free Multi-Class Class-agnostic Counting](https://mlsanity.com/paper/2309.04820/) (2023)
- [Every Shot Counts: Using Exemplars for Repetition Counting in Videos](https://mlsanity.com/paper/2403.18074/) (2024)
- [Single Domain Generalization for Few-Shot Counting via Universal Representation Matching](https://mlsanity.com/paper/2505.16778/) (2025)
- [Learning to Count without Annotations](https://mlsanity.com/paper/2307.08727/) (2023)
- [Learning Spatial Similarity Distribution for Few-shot Object Counting](https://mlsanity.com/paper/2405.11770/) (2024)
- [Few-Shot Pattern Detection via Template Matching and Regression](https://mlsanity.com/paper/2508.17636/) (2025)

## Abstract (arXiv)

This paper aims to tackle the challenging task of one-shot object counting. Given an image containing novel, previously unseen category objects, the goal of the task is to count all instances in the desired category with only one supporting bounding box example. To this end, we propose a counting model by which you only need to Look At One instance (LaoNet). First, a feature correlation module combines the Self-Attention and Correlative-Attention modules to learn both inner-relations and inter-relations. It enables the network to be robust to the inconsistency of rotations and sizes among different instances. Second, a Scale Aggregation mechanism is designed to help extract features with different scale information. Compared with existing few-shot counting methods, LaoNet achieves state-of-the-art results while learning with a high convergence speed. The code will be available soon.