# Crowdsourcing of Real-world Image Annotation via Visual Properties

arXiv: https://arxiv.org/abs/2604.14449 · HTML: https://mlsanity.com/paper/2604.14449/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 23 of its 44 references that are in the map.
- First posted on arXiv 15 Apr 2026.

## Builds on (most cited first, 23 of 23)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [Visualizing and Understanding Convolutional Networks](https://mlsanity.com/paper/1311.2901/) (2013)
- [Residual Attention Network for Image Classification](https://mlsanity.com/paper/1704.06904/) (2017)
- [Towards Fairer Datasets: Filtering and Balancing the Distribution of the People Subtree in the ImageNet Hierarchy](https://mlsanity.com/paper/1912.07726/) (2019)
- [Re-labeling ImageNet: from Single to Multi-Labels, from Global to Localized Labels](https://mlsanity.com/paper/2101.05022/) (2021)
- [From ImageNet to Image Classification: Contextualizing Progress on Benchmarks](https://mlsanity.com/paper/2005.11295/) (2020)
- [Data-Centric Artificial Intelligence](https://mlsanity.com/paper/2212.11854/) (2022)
- [Toward Zero-shot Character Recognition: A Gold Standard Dataset with Radical-level Annotations](https://mlsanity.com/paper/2308.00655/) (2023)
- [Assessing Data Quality of Annotations with Krippendorff Alpha For Applications in Computer Vision](https://mlsanity.com/paper/1912.10107/) (2019)
- [krippendorffsalpha: An R Package for Measuring Agreement Using Krippendorff's Alpha Coefficient](https://mlsanity.com/paper/2103.12170/) (2021)
- [Aligning Visual and Lexical Semantics](https://mlsanity.com/paper/2212.06629/) (2022)
- [Managing Bias in Human-Annotated Data: Moving Beyond Bias Removal](https://mlsanity.com/paper/2110.13504/) (2021)
- [A semantics-driven methodology for high-quality image annotation](https://mlsanity.com/paper/2307.14119/) (2023)
- [Competitive Distillation: A Simple Learning Strategy for Improving Visual Classification](https://mlsanity.com/paper/2506.23285/) (2025)
- [Building a visual semantics aware object hierarchy](https://mlsanity.com/paper/2202.13021/) (2022)
- [Incremental Image Labeling via Iterative Refinement](https://mlsanity.com/paper/2304.08989/) (2023)
- [KAE: A Property-based Method for Knowledge Graph Alignment and Extension](https://mlsanity.com/paper/2407.05320/) (2024)

## Abstract (arXiv)

Recent advances in data-centric artificial intelligence highlight inherent limitations in object recognition datasets. One of the primary issues stems from the semantic gap problem, which results in complex many-to-many mappings between visual data and linguistic descriptions. This bias adversely affects performance in computer vision tasks. This paper proposes an image annotation methodology that integrates knowledge representation, natural language processing, and computer vision techniques, aiming to reduce annotator subjectivity by applying visual property constraints. We introduce an interactive crowdsourcing framework that dynamically asks questions based on a predefined object category hierarchy and annotator feedback, guiding image annotation by visual properties. Experiments demonstrate the effectiveness of this methodology, and annotator feedback is discussed to optimize the crowdsourcing setup.