# Zero-shot Object Counting with Good Exemplars

arXiv: https://arxiv.org/abs/2407.04948 · HTML: https://mlsanity.com/paper/2407.04948/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 39 of its 51 references that are in the map.
- First posted on arXiv 6 Jul 2024.

## Builds on (most cited first, 30 of 39)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://mlsanity.com/paper/2301.12597/) (2023)
- [BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://mlsanity.com/paper/2201.12086/) (2022)
- [Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection](https://mlsanity.com/paper/2303.05499/) (2023)
- [Learning to Prompt for Open-Vocabulary Object Detection with Vision-Language Model](https://mlsanity.com/paper/2203.14940/) (2022)
- [Delving into Out-of-Distribution Detection with Vision-Language Representations](https://mlsanity.com/paper/2211.13445/) (2022)
- [Zero-Shot Object Detection](https://mlsanity.com/paper/1804.04340/) (2018)
- [CLIP-ReID: Exploiting Vision-Language Model for Image Re-Identification without Concrete Text Labels](https://mlsanity.com/paper/2211.13977/) (2022)
- [Drone-based Object Counting by Spatially Regularized Regional Proposal Network](https://mlsanity.com/paper/1707.05972/) (2017)
- [Learning To Count Everything](https://mlsanity.com/paper/2104.08391/) (2021)
- [Coarse-to-Fine Vision-Language Pre-training with Fusion in the Backbone](https://mlsanity.com/paper/2206.07643/) (2022)
- [A Large Contextual Dataset for Classification, Detection and Counting of Cars with Deep Learning](https://mlsanity.com/paper/1609.04453/) (2016)
- [Class-Agnostic Counting](https://mlsanity.com/paper/1811.00472/) (2018)
- [CLIP-Count: Towards Text-Guided Zero-Shot Object Counting](https://mlsanity.com/paper/2305.07304/) (2023)
- [Represent, Compare, and Learn: A Similarity-Aware Framework for Class-Agnostic Counting](https://mlsanity.com/paper/2203.08354/) (2022)
- [CounTR: Transformer-based Generalised Visual Counting](https://mlsanity.com/paper/2208.13721/) (2022)
- [A Low-Shot Object Counting Network With Iterative Prototype Adaptation](https://mlsanity.com/paper/2211.08217/) (2022)
- [Few-shot Object Counting with Similarity-Aware Feature Enhancement](https://mlsanity.com/paper/2201.08959/) (2022)
- [Zero-shot Object Counting](https://mlsanity.com/paper/2303.02001/) (2023)
- [Few-shot Object Counting and Detection](https://mlsanity.com/paper/2207.10988/) (2022)
- [RaSa: Relation and Sensitivity Aware Representation Learning for Text-based Person Search](https://mlsanity.com/paper/2305.13653/) (2023)
- [Iterative Crowd Counting](https://mlsanity.com/paper/1807.09959/) (2018)
- [VLCounter: Text-aware Visual Representation for Zero-Shot Object Counting](https://mlsanity.com/paper/2312.16580/) (2023)
- [Learning to Count Anything: Reference-less Class-agnostic Counting with Weak Supervision](https://mlsanity.com/paper/2205.10203/) (2022)
- [Zero-Shot Instance Segmentation](https://mlsanity.com/paper/2104.06601/) (2021)
- [Point, Segment and Count: A Generalized Framework for Object Counting](https://mlsanity.com/paper/2311.12386/) (2023)
- [Training-free Object Counting with Prompts](https://mlsanity.com/paper/2307.00038/) (2023)
- [Exemplar Free Class Agnostic Counting](https://mlsanity.com/paper/2205.14212/) (2022)
- [Adaptive Mixture Regression Network with Local Counting Map for Crowd Counting](https://mlsanity.com/paper/2005.05776/) (2020)

## Built on by (most cited first, 21 of 21)

- [DisasterM3: A Remote Sensing Vision-Language Dataset for Disaster Damage Assessment and Response](https://mlsanity.com/paper/2505.21089/) (2025)
- [T2ICount: Enhancing Cross-modal Understanding for Zero-Shot Counting](https://mlsanity.com/paper/2502.20625/) (2025)
- [YOLO-Count: Differentiable Object Counting for Text-to-Image Generation](https://mlsanity.com/paper/2508.00728/) (2025)
- [A Survey on Class-Agnostic Counting: Advancements from Reference-Based to Open-World Text-Guided Approaches](https://mlsanity.com/paper/2501.19184/) (2025)
- [Can Current AI Models Count What We Mean, Not What They See? A Benchmark and Systematic Evaluation](https://mlsanity.com/paper/2509.13939/) (2025)
- [Single Domain Generalization for Few-Shot Counting via Universal Representation Matching](https://mlsanity.com/paper/2505.16778/) (2025)
- [Instruct2See: Learning to Remove Any Obstructions Across Distributions](https://mlsanity.com/paper/2505.17649/) (2025)
- [FiGO: Fine-Grained Object Counting without Annotations](https://mlsanity.com/paper/2504.11705/) (2025)
- [Text-promptable Object Counting via Quantity Awareness Enhancement](https://mlsanity.com/paper/2507.06679/) (2025)
- [Bootstrapping MLLM for Weakly-Supervised Class-Agnostic Object Counting](https://mlsanity.com/paper/2602.12774/) (2026)
- [Consistent-Point: Consistent Pseudo-Points for Semi-Supervised Crowd Counting and Localization](https://mlsanity.com/paper/2503.12441/) (2025)
- [Expanding Zero-Shot Object Counting with Rich Prompts](https://mlsanity.com/paper/2505.15398/) (2025)
- [Object Counting with GPT-4o and GPT-5: A Comparative Study](https://mlsanity.com/paper/2512.03233/) (2025)
- [CountEx: Fine-Grained Counting via Exemplars and Exclusion](https://mlsanity.com/paper/2602.19432/) (2026)
- [AdaCount: Training-Free Similarity-Guided Spatial and Feature Adaptation for Zero-Shot Object Counting](https://mlsanity.com/paper/2607.02139/) (2026)
- [FocalCount: Towards Class-Count Imbalance in Class-Agnostic Counting](https://mlsanity.com/paper/2502.10677/) (2025)
- [CountZES: Counting via Zero-Shot Exemplar Selection](https://mlsanity.com/paper/2512.16415/) (2025)
- [Count Anything](https://mlsanity.com/paper/2605.30846/) (2026)
- [RT-Counter: Real-Time Text-Guided Open-Vocabulary Object Counting](https://mlsanity.com/paper/2606.17561/) (2026)
- [RECOUNT: Reference-guided Counting with Synthetic Visual Exemplars](https://mlsanity.com/paper/2608.20621/) (2026)
- [DualCount: Structurally Consistent Density and Point Modeling for Zero-Shot Object Counting](https://mlsanity.com/paper/2609.17613/) (2026)

## Abstract (arXiv)

Zero-shot object counting (ZOC) aims to enumerate objects in images using only the names of object classes during testing, without the need for manual annotations. However, a critical challenge in current ZOC methods lies in their inability to identify high-quality exemplars effectively. This deficiency hampers scalability across diverse classes and undermines the development of strong visual associations between the identified classes and image content. To this end, we propose the Visual Association-based Zero-shot Object Counting (VA-Count) framework. VA-Count consists of an Exemplar Enhancement Module (EEM) and a Noise Suppression Module (NSM) that synergistically refine the process of class exemplar identification while minimizing the consequences of incorrect object identification. The EEM utilizes advanced vision-language pretaining models to discover potential exemplars, ensuring the framework's adaptability to various classes. Meanwhile, the NSM employs contrastive learning to differentiate between optimal and suboptimal exemplar pairs, reducing the negative effects of erroneous exemplars. VA-Count demonstrates its effectiveness and scalability in zero-shot contexts with superior performance on two object counting datasets.