# How hard can it be? Estimating the difficulty of visual search in an image

arXiv: https://arxiv.org/abs/1705.08280 · HTML: https://mlsanity.com/paper/1705.08280/

## Key facts

- Cited by 52 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 May 2017.

## Built on by (most cited first, 30 of 52)

- [Curriculum Learning: A Survey](https://mlsanity.com/paper/2101.10382/) (2021)
- [EfficientTrain: Exploring Generalized Curriculum Learning for Training Visual Backbones](https://mlsanity.com/paper/2211.09703/) (2022)
- [Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples](https://mlsanity.com/paper/2502.09650/) (2025)
- [Embracing the Dark Knowledge: Domain Generalization Using Regularized Knowledge Distillation](https://mlsanity.com/paper/2107.02629/) (2021)
- [Learning to Solve Travelling Salesman Problem with Hardness-adaptive Curriculum](https://mlsanity.com/paper/2204.03236/) (2022)
- [Difficulty-Aware Simulator for Open Set Recognition](https://mlsanity.com/paper/2207.10024/) (2022)
- [CL-MAE: Curriculum-Learned Masked Autoencoders](https://mlsanity.com/paper/2308.16572/) (2023)
- [Coaching a Teachable Student](https://mlsanity.com/paper/2306.10014/) (2023)
- [EfficientTrain++: Generalized Curriculum Learning for Efficient Visual Backbone Training](https://mlsanity.com/paper/2405.08768/) (2024)
- [Membership Inference Attacks are Easier on Difficult Problems](https://mlsanity.com/paper/2102.07762/) (2021)
- [RealCustom++: Representing Images as Real Textual Word for Real-Time Customization](https://mlsanity.com/paper/2408.09744/) (2024)
- [CTBENCH: A Library and Benchmark for Certified Training](https://mlsanity.com/paper/2406.04848/) (2024)
- [Intent-based Prompt Calibration: Enhancing prompt optimization with synthetic boundary cases](https://mlsanity.com/paper/2402.03099/) (2024)
- [From Easy to Hard: Progressive Active Learning Framework for Infrared Small Target Detection with Single Point Supervision](https://mlsanity.com/paper/2412.11154/) (2024)
- [When Deep Classifiers Agree: Analyzing Correlations between Learning Order and Image Statistics](https://mlsanity.com/paper/2105.08997/) (2021)
- [Unsupervised Medical Image Alignment with Curriculum Learning](https://mlsanity.com/paper/2102.10438/) (2021)
- [Learning Rate Curriculum](https://mlsanity.com/paper/2205.09180/) (2022)
- [Learning to Learn: How to Continuously Teach Humans and Machines](https://mlsanity.com/paper/2211.15470/) (2022)
- [RecRecNet: Rectangling Rectified Wide-Angle Images by Thin-Plate Spline Model and DoF-based Curriculum Learning](https://mlsanity.com/paper/2301.01661/) (2023)
- [Reverse Stable Diffusion: What prompt was used to generate this image?](https://mlsanity.com/paper/2308.01472/) (2023)
- [Stochastic Re-weighted Gradient Descent via Distributionally Robust Optimization](https://mlsanity.com/paper/2306.09222/) (2023)
- [Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and Anticipation](https://mlsanity.com/paper/2411.13059/) (2024)
- [Toddler-Guidance Learning: Impacts of Critical Period on Multimodal AI Agents](https://mlsanity.com/paper/2201.04990/) (2022)
- [Sample selection for efficient image annotation](https://mlsanity.com/paper/2105.04678/) (2021)
- [Neglected Free Lunch -- Learning Image Classifiers Using Annotation Byproducts](https://mlsanity.com/paper/2303.17595/) (2023)
- [A Lightweight Measure of Classification Difficulty from Application Dataset Characteristics](https://mlsanity.com/paper/2404.05981/) (2024)
- [Was that so hard? Estimating human classification difficulty](https://mlsanity.com/paper/2203.11824/) (2022)
- [CBM: Curriculum by Masking](https://mlsanity.com/paper/2407.05193/) (2024)
- [Understanding Complexity in VideoQA via Visual Program Generation](https://mlsanity.com/paper/2505.13429/) (2025)
- [Demystifying Data Organization for Enhanced LLM Training](https://mlsanity.com/paper/2605.30334/) (2026)

## Abstract (arXiv)

We address the problem of estimating image difficulty defined as the human response time for solving a visual search task. We collect human annotations of image difficulty for the PASCAL VOC 2012 data set through a crowd-sourcing platform. We then analyze what human interpretable image properties can have an impact on visual search difficulty, and how accurate are those properties for predicting difficulty. Next, we build a regression model based on deep features learned with state of the art convolutional neural networks and show better results for predicting the ground-truth visual search difficulty scores produced by human annotators. Our model is able to correctly rank about 75% image pairs according to their difficulty score. We also show that our difficulty predictor generalizes well to new classes not seen during training. Finally, we demonstrate that our predicted difficulty scores are useful for weakly supervised object localization (8% improvement) and semi-supervised object classification (1% improvement).