# Count-ception: Counting by Fully Convolutional Redundant Counting

arXiv: https://arxiv.org/abs/1703.08710 · HTML: https://mlsanity.com/paper/1703.08710/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Mar 2017.

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

- [LVLM-COUNT: Enhancing the Counting Ability of Large Vision-Language Models](https://mlsanity.com/paper/2412.00686/) (2024)
- [Discrete-Constrained Regression for Local Counting Models](https://mlsanity.com/paper/2207.09865/) (2022)
- [DeGPR: Deep Guided Posterior Regularization for Multi-Class Cell Detection and Counting](https://mlsanity.com/paper/2304.00741/) (2023)
- [Redesigning Multi-Scale Neural Network for Crowd Counting](https://mlsanity.com/paper/2208.02894/) (2022)
- [Mind the Prompt: A Novel Benchmark for Prompt-based Class-Agnostic Counting](https://mlsanity.com/paper/2409.15953/) (2024)
- [Behind the leaves -- Estimation of occluded grapevine berries with conditional generative adversarial networks](https://mlsanity.com/paper/2105.10325/) (2021)
- [DAWN: Domain-Adaptive Weakly Supervised Nuclei Segmentation via Cross-Task Interactions](https://mlsanity.com/paper/2404.14956/) (2024)
- [A Survey on Class-Agnostic Counting: Advancements from Reference-Based to Open-World Text-Guided Approaches](https://mlsanity.com/paper/2501.19184/) (2025)
- [A Spatio-Temporal Attentive Network for Video-Based Crowd Counting](https://mlsanity.com/paper/2208.11339/) (2022)
- [Rethinking Cell Counting Methods: Decoupling Counting and Localization](https://mlsanity.com/paper/2503.13989/) (2025)
- [IDCIA: Immunocytochemistry Dataset for Cellular Image Analysis](https://mlsanity.com/paper/2411.08992/) (2024)
- [Cross-domain Microscopy Cell Counting by Disentangled Transfer Learning](https://mlsanity.com/paper/2211.14638/) (2022)
- [Shifted Autoencoders for Point Annotation Restoration in Object Counting](https://mlsanity.com/paper/2312.07190/) (2023)
- [PerSense: Training-Free Personalized Instance Segmentation in Dense Images](https://mlsanity.com/paper/2405.13518/) (2024)
- [Towards PerSense++: Advancing Training-Free Personalized Instance Segmentation in Dense Images](https://mlsanity.com/paper/2508.14660/) (2025)
- [Does it Really Count? Assessing Semantic Grounding in Text-Guided Class-Agnostic Counting](https://mlsanity.com/paper/2605.02752/) (2026)
- [Improved Counting and Localization from Density Maps for Object Detection in 2D and 3D Microscopy Imaging](https://mlsanity.com/paper/2203.15691/) (2022)
- [Deep Learning Techniques for Visual Counting](https://mlsanity.com/paper/2206.03033/) (2022)
- [CellFMCount: A Fluorescence Microscopy Dataset, Benchmark, and Methods for Cell Counting](https://mlsanity.com/paper/2511.19351/) (2025)
- [CountXplain: Interpretable Cell Counting with Prototype-Based Density Map Estimation](https://mlsanity.com/paper/2511.19686/) (2025)
- [NucFuseRank: Dataset Fusion and Performance Ranking for Nuclei Instance Segmentation](https://mlsanity.com/paper/2601.20104/) (2026)
- [AdaCount: Training-Free Similarity-Guided Spatial and Feature Adaptation for Zero-Shot Object Counting](https://mlsanity.com/paper/2607.02139/) (2026)
- [Weakly-supervised multi-class object localization using only object counts as labels](https://mlsanity.com/paper/2102.11743/) (2021)
- [Automatic Cell Counting in Flourescent Microscopy Using Deep Learning](https://mlsanity.com/paper/2103.01141/) (2021)
- [Cellcounter: a deep learning framework for high-fidelity spatial localization of neurons](https://mlsanity.com/paper/2103.10462/) (2021)
- [Single Image Object Counting and Localizing using Active-Learning](https://mlsanity.com/paper/2111.08383/) (2021)
- [DLA-Count: Dynamic Label Assignment Network for Dense Cell Distribution Counting](https://mlsanity.com/paper/2503.12063/) (2025)
- [Towards Conditional Feature Alignment for Cross-Domain Counting](https://mlsanity.com/paper/2506.17137/) (2025)
- [CountZES: Counting via Zero-Shot Exemplar Selection](https://mlsanity.com/paper/2512.16415/) (2025)
- [MicroscopyMatching: Towards a Ready-to-use Framework for Microscopy Image Analysis in Diverse Conditions](https://mlsanity.com/paper/2605.14980/) (2026)

## Abstract (arXiv)

Counting objects in digital images is a process that should be replaced by machines. This tedious task is time consuming and prone to errors due to fatigue of human annotators. The goal is to have a system that takes as input an image and returns a count of the objects inside and justification for the prediction in the form of object localization. We repose a problem, originally posed by Lempitsky and Zisserman, to instead predict a count map which contains redundant counts based on the receptive field of a smaller regression network. The regression network predicts a count of the objects that exist inside this frame. By processing the image in a fully convolutional way each pixel is going to be accounted for some number of times, the number of windows which include it, which is the size of each window, (i.e., 32x32 = 1024). To recover the true count we take the average over the redundant predictions. Our contribution is redundant counting instead of predicting a density map in order to average over errors. We also propose a novel deep neural network architecture adapted from the Inception family of networks called the Count-ception network. Together our approach results in a 20% relative improvement (2.9 to 2.3 MAE) over the state of the art method by Xie, Noble, and Zisserman in 2016.