# Counting Everyday Objects in Everyday Scenes

arXiv: https://arxiv.org/abs/1604.03505 · HTML: https://mlsanity.com/paper/1604.03505/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Apr 2016.

## Built on by (most cited first, 30 of 34)

- [QA Dataset Explosion: A Taxonomy of NLP Resources for Question Answering and Reading Comprehension](https://mlsanity.com/paper/2107.12708/) (2021)
- [Will Large-scale Generative Models Corrupt Future Datasets?](https://mlsanity.com/paper/2211.08095/) (2022)
- [Zero-shot Object Counting](https://mlsanity.com/paper/2303.02001/) (2023)
- [VLCounter: Text-aware Visual Representation for Zero-Shot Object Counting](https://mlsanity.com/paper/2312.16580/) (2023)
- [KiVA: Kid-inspired Visual Analogies for Testing Large Multimodal Models](https://mlsanity.com/paper/2407.17773/) (2024)
- [LVLM-COUNT: Enhancing the Counting Ability of Large Vision-Language Models](https://mlsanity.com/paper/2412.00686/) (2024)
- [OmniCount: Multi-label Object Counting with Semantic-Geometric Priors](https://mlsanity.com/paper/2403.05435/) (2024)
- [Object Counting: You Only Need to Look at One](https://mlsanity.com/paper/2112.05993/) (2021)
- [Scene-Adaptive Attention Network for Crowd Counting](https://mlsanity.com/paper/2112.15509/) (2021)
- [Zero-Shot Object Counting with Language-Vision Models](https://mlsanity.com/paper/2309.13097/) (2023)
- [Every Shot Counts: Using Exemplars for Repetition Counting in Videos](https://mlsanity.com/paper/2403.18074/) (2024)
- [AGAR a microbial colony dataset for deep learning detection](https://mlsanity.com/paper/2108.01234/) (2021)
- [Counting Stacked Objects](https://mlsanity.com/paper/2411.19149/) (2024)
- [TreeFormer: a Semi-Supervised Transformer-based Framework for Tree Counting from a Single High Resolution Image](https://mlsanity.com/paper/2307.06118/) (2023)
- [CountingDINO: A Training-free Pipeline for Class-Agnostic Counting using Unsupervised Backbones](https://mlsanity.com/paper/2504.16570/) (2025)
- [Visual Enumeration Remains Challenging for Multimodal Generative AI](https://mlsanity.com/paper/2402.03328/) (2024)
- [GCNet: Probing Self-Similarity Learning for Generalized Counting Network](https://mlsanity.com/paper/2302.05132/) (2023)
- [Towards Generalization in Subitizing with Neuro-Symbolic Loss using Holographic Reduced Representations](https://mlsanity.com/paper/2312.15310/) (2023)
- [What is the Right Embedding Space for Contrastive Learning in Referring Expression Counting?](https://mlsanity.com/paper/2505.22850/) (2025)
- [Bootstrapping MLLM for Weakly-Supervised Class-Agnostic Object Counting](https://mlsanity.com/paper/2602.12774/) (2026)
- [Self-Normalized Density Map (SNDM) for Counting Microbiological Objects](https://mlsanity.com/paper/2203.09474/) (2022)
- [Virtual Classification: Modulating Domain-Specific Knowledge for Multidomain Crowd Counting](https://mlsanity.com/paper/2402.03758/) (2024)
- [Enhancing Zero-shot Counting via Language-guided Exemplar Learning](https://mlsanity.com/paper/2402.05394/) (2024)
- [Querying Autonomous Vehicle Point Clouds: Enhanced by 3D Object Counting with CounterNet](https://mlsanity.com/paper/2507.19209/) (2025)
- [Automated Counting of Stacked Objects in Industrial Inspection](https://mlsanity.com/paper/2603.15470/) (2026)
- [The Count Is There, but Misaligned: Understanding and Correcting Counting Failures in VLMs](https://mlsanity.com/paper/2607.09544/) (2026)
- [Detecting Cattle and Elk in the Wild from Space](https://mlsanity.com/paper/2106.15448/) (2021)
- [CMR3D: Contextualized Multi-Stage Refinement for 3D Object Detection](https://mlsanity.com/paper/2209.06641/) (2022)
- [Learning from Pseudo-labeled Segmentation for Multi-Class Object Counting](https://mlsanity.com/paper/2307.07677/) (2023)
- [Contrastive Learning and Abstract Concepts: The Case of Natural Numbers](https://mlsanity.com/paper/2408.02247/) (2024)

## Abstract (arXiv)

We are interested in counting the number of instances of object classes in natural, everyday images. Previous counting approaches tackle the problem in restricted domains such as counting pedestrians in surveillance videos. Counts can also be estimated from outputs of other vision tasks like object detection. In this work, we build dedicated models for counting designed to tackle the large variance in counts, appearances, and scales of objects found in natural scenes. Our approach is inspired by the phenomenon of subitizing - the ability of humans to make quick assessments of counts given a perceptual signal, for small count values. Given a natural scene, we employ a divide and conquer strategy while incorporating context across the scene to adapt the subitizing idea to counting. Our approach offers consistent improvements over numerous baseline approaches for counting on the PASCAL VOC 2007 and COCO datasets. Subsequently, we study how counting can be used to improve object detection. We then show a proof of concept application of our counting methods to the task of Visual Question Answering, by studying the `how many?' questions in the VQA and COCO-QA datasets.