# Incremental Image Labeling via Iterative Refinement

arXiv: https://arxiv.org/abs/2304.08989 · HTML: https://mlsanity.com/paper/2304.08989/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 27 references that are in the map.
- First posted on arXiv 18 Apr 2023.

## Builds on (most cited first, 16 of 16)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [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)
- [Residual Attention Network for Image Classification](https://mlsanity.com/paper/1704.06904/) (2017)
- [Are we done with ImageNet?](https://mlsanity.com/paper/2006.07159/) (2020)
- [No Classification without Representation: Assessing Geodiversity Issues in Open Data Sets for the Developing World](https://mlsanity.com/paper/1711.08536/) (2017)
- [Does Object Recognition Work for Everyone?](https://mlsanity.com/paper/1906.02659/) (2019)
- [From ImageNet to Image Classification: Contextualizing Progress on Benchmarks](https://mlsanity.com/paper/2005.11295/) (2020)
- [Towards Visual Semantics](https://mlsanity.com/paper/2104.12379/) (2021)
- [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)
- [Visual Ground Truth Construction as Faceted Classification](https://mlsanity.com/paper/2202.08512/) (2022)
- [Building a visual semantics aware object hierarchy](https://mlsanity.com/paper/2202.13021/) (2022)

## Built on by (most cited first, 3 of 3)

- [A semantics-driven methodology for high-quality image annotation](https://mlsanity.com/paper/2307.14119/) (2023)
- [What can Computer Vision learn from Ranganathan?](https://mlsanity.com/paper/2601.22634/) (2026)
- [Crowdsourcing of Real-world Image Annotation via Visual Properties](https://mlsanity.com/paper/2604.14449/) (2026)

## Abstract (arXiv)

Data quality is critical for multimedia tasks, while various types of systematic flaws are found in image benchmark datasets, as discussed in recent work. In particular, the existence of the semantic gap problem leads to a many-to-many mapping between the information extracted from an image and its linguistic description. This unavoidable bias further leads to poor performance on current computer vision tasks. To address this issue, we introduce a Knowledge Representation (KR)-based methodology to provide guidelines driving the labeling process, thereby indirectly introducing intended semantics in ML models. Specifically, an iterative refinement-based annotation method is proposed to optimize data labeling by organizing objects in a classification hierarchy according to their visual properties, ensuring that they are aligned with their linguistic descriptions. Preliminary results verify the effectiveness of the proposed method.