# Salient Object Subitizing

arXiv: https://arxiv.org/abs/1607.07525 · HTML: https://mlsanity.com/paper/1607.07525/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jul 2016.

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

- [Teaching CLIP to Count to Ten](https://mlsanity.com/paper/2302.12066/) (2023)
- [Multimodal Contrastive Training for Visual Representation Learning](https://mlsanity.com/paper/2104.12836/) (2021)
- [Salient Objects in Clutter](https://mlsanity.com/paper/2105.03053/) (2021)
- [RPCANet++: Deep Interpretable Robust PCA for Sparse Object Segmentation](https://mlsanity.com/paper/2508.04190/) (2025)
- [Weakly-Supervised Saliency Detection via Salient Object Subitizing](https://mlsanity.com/paper/2101.00932/) (2021)
- [ViDSOD-100: A New Dataset and a Baseline Model for RGB-D Video Salient Object Detection](https://mlsanity.com/paper/2406.12536/) (2024)
- [Learning to Count without Annotations](https://mlsanity.com/paper/2307.08727/) (2023)
- [Towards Generalization in Subitizing with Neuro-Symbolic Loss using Holographic Reduced Representations](https://mlsanity.com/paper/2312.15310/) (2023)
- [Pluralistic Salient Object Detection](https://mlsanity.com/paper/2409.02368/) (2024)
- [Horizontal-to-Vertical Video Conversion](https://mlsanity.com/paper/2101.04051/) (2021)
- [Do MLLMs Exhibit Human-like Perceptual Behaviors? HVSBench: A Benchmark for MLLM Alignment with Human Perceptual Behavior](https://mlsanity.com/paper/2412.09603/) (2024)
- [Understanding the Ability of Deep Neural Networks to Count Connected Components in Images](https://mlsanity.com/paper/2101.01386/) (2021)
- [Revisiting Salient Object Detection from an Observer-Centric Perspective](https://mlsanity.com/paper/2602.06369/) (2026)

## Abstract (arXiv)

We study the problem of Salient Object Subitizing, i.e. predicting the existence and the number of salient objects in an image using holistic cues. This task is inspired by the ability of people to quickly and accurately identify the number of items within the subitizing range (1-4). To this end, we present a salient object subitizing image dataset of about 14K everyday images which are annotated using an online crowdsourcing marketplace. We show that using an end-to-end trained Convolutional Neural Network (CNN) model, we achieve prediction accuracy comparable to human performance in identifying images with zero or one salient object. For images with multiple salient objects, our model also provides significantly better than chance performance without requiring any localization process. Moreover, we propose a method to improve the training of the CNN subitizing model by leveraging synthetic images. In experiments, we demonstrate the accuracy and generalizability of our CNN subitizing model and its applications in salient object detection and image retrieval.