# Finding any Waldo: zero-shot invariant and efficient visual search

arXiv: https://arxiv.org/abs/1807.10587 · HTML: https://mlsanity.com/paper/1807.10587/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jul 2018.

## Built on by (most cited first, 28 of 28)

- [V*: Guided Visual Search as a Core Mechanism in Multimodal LLMs](https://mlsanity.com/paper/2312.14135/) (2023)
- [Predicting Goal-directed Human Attention Using Inverse Reinforcement Learning](https://mlsanity.com/paper/2005.14310/) (2020)
- [DyFo: A Training-Free Dynamic Focus Visual Search for Enhancing LMMs in Fine-Grained Visual Understanding](https://mlsanity.com/paper/2504.14920/) (2025)
- [Gazeformer: Scalable, Effective and Fast Prediction of Goal-Directed Human Attention](https://mlsanity.com/paper/2303.15274/) (2023)
- [Training-free Object Counting with Prompts](https://mlsanity.com/paper/2307.00038/) (2023)
- [Unifying Top-down and Bottom-up Scanpath Prediction Using Transformers](https://mlsanity.com/paper/2303.09383/) (2023)
- [OmniCount: Multi-label Object Counting with Semantic-Geometric Priors](https://mlsanity.com/paper/2403.05435/) (2024)
- [Visual Search Asymmetry: Deep Nets and Humans Share Similar Inherent Biases](https://mlsanity.com/paper/2106.02953/) (2021)
- [Look Twice: A Generalist Computational Model Predicts Return Fixations across Tasks and Species](https://mlsanity.com/paper/2101.01611/) (2021)
- [Efficient Zero-shot Visual Search via Target and Context-aware Transformer](https://mlsanity.com/paper/2211.13470/) (2022)
- [CoFFT: Chain of Foresight-Focus Thought for Visual Language Models](https://mlsanity.com/paper/2509.22010/) (2025)
- [Flow Snapshot Neurons in Action: Deep Neural Networks Generalize to Biological Motion Perception](https://mlsanity.com/paper/2405.16493/) (2024)
- [Peering into the Unknown: Active View Selection with Neural Uncertainty Maps for 3D Reconstruction](https://mlsanity.com/paper/2506.14856/) (2025)
- [DRS-GUI: Dynamic Region Search for Training-Free GUI Grounding](https://mlsanity.com/paper/2605.15542/) (2026)
- [TPP-Gaze: Modelling Gaze Dynamics in Space and Time with Neural Temporal Point Processes](https://mlsanity.com/paper/2410.23409/) (2024)
- [Learning to See the Elephant in the Room: Self-Supervised Context Reasoning in Humans and AI](https://mlsanity.com/paper/2211.12817/) (2022)
- [Learning to search for and detect objects in foveal images using deep learning](https://mlsanity.com/paper/2304.05741/) (2023)
- [Gazing at Rewards: Eye Movements as a Lens into Human and AI Decision-Making in Hybrid Visual Foraging](https://mlsanity.com/paper/2411.09176/) (2024)
- [Human Scanpath Prediction in Target-Present Visual Search with Semantic-Foveal Bayesian Attention](https://mlsanity.com/paper/2507.18503/) (2025)
- [Semantic-Based Active Perception for Humanoid Visual Tasks with Foveal Sensors](https://mlsanity.com/paper/2404.10836/) (2024)
- [GazeSearch: Radiology Findings Search Benchmark](https://mlsanity.com/paper/2411.05780/) (2024)
- [CNN-based search model underestimates attention guidance by simple visual features](https://mlsanity.com/paper/2103.15439/) (2021)
- [Benchmarking human visual search computational models in natural scenes: models comparison and reference datasets](https://mlsanity.com/paper/2112.05808/) (2021)
- [Predicting Visual Attention and Distraction During Visual Search Using Convolutional Neural Networks](https://mlsanity.com/paper/2210.15093/) (2022)
- [Can Machines Imitate Humans? Integrative Turing-like tests for Language and Vision Demonstrate a Narrowing Gap](https://mlsanity.com/paper/2211.13087/) (2022)
- [Saccade crossing avoidance as a visual search strategy](https://mlsanity.com/paper/2508.18404/) (2025)
- [Cost-Efficient Multi-Scale Fovea for Semantic-Based Visual Search Attention](https://mlsanity.com/paper/2604.03836/) (2026)
- [Task-driven Processing with Coarse-to-Fine Glimpse-based Active Perception](https://mlsanity.com/paper/2609.09025/) (2026)

## Abstract (arXiv)

Searching for a target object in a cluttered scene constitutes a fundamental challenge in daily vision. Visual search must be selective enough to discriminate the target from distractors, invariant to changes in the appearance of the target, efficient to avoid exhaustive exploration of the image, and must generalize to locate novel target objects with zero-shot training. Previous work has focused on searching for perfect matches of a target after extensive category-specific training. Here we show for the first time that humans can efficiently and invariantly search for natural objects in complex scenes. To gain insight into the mechanisms that guide visual search, we propose a biologically inspired computational model that can locate targets without exhaustive sampling and generalize to novel objects. The model provides an approximation to the mechanisms integrating bottom-up and top-down signals during search in natural scenes.