# Learning to count with deep object features

arXiv: https://arxiv.org/abs/1505.08082 · HTML: https://mlsanity.com/paper/1505.08082/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 May 2015.

## Built on by (most cited first, 8 of 8)

- [Text encoders bottleneck compositionality in contrastive vision-language models](https://mlsanity.com/paper/2305.14897/) (2023)
- [Every Shot Counts: Using Exemplars for Repetition Counting in Videos](https://mlsanity.com/paper/2403.18074/) (2024)
- [Quantity Matters: Towards Assessing and Mitigating Number Hallucination in Large Vision-Language Models](https://mlsanity.com/paper/2403.01373/) (2024)
- [Multi-Class Multi-Instance Count Conditioned Adversarial Image Generation](https://mlsanity.com/paper/2103.16795/) (2021)
- [Automatic Cell Counting in Flourescent Microscopy Using Deep Learning](https://mlsanity.com/paper/2103.01141/) (2021)
- [Single Image Object Counting and Localizing using Active-Learning](https://mlsanity.com/paper/2111.08383/) (2021)
- [CMR3D: Contextualized Multi-Stage Refinement for 3D Object Detection](https://mlsanity.com/paper/2209.06641/) (2022)
- [Wheat Head Counting by Estimating a Density Map with Convolutional Neural Networks](https://mlsanity.com/paper/2303.10542/) (2023)

## Abstract (arXiv)

Learning to count is a learning strategy that has been recently proposed in the literature for dealing with problems where estimating the number of object instances in a scene is the final objective. In this framework, the task of learning to detect and localize individual object instances is seen as a harder task that can be evaded by casting the problem as that of computing a regression value from hand-crafted image features. In this paper we explore the features that are learned when training a counting convolutional neural network in order to understand their underlying representation. To this end we define a counting problem for MNIST data and show that the internal representation of the network is able to classify digits in spite of the fact that no direct supervision was provided for them during training. We also present preliminary results about a deep network that is able to count the number of pedestrians in a scene.