# Aligning Visual and Lexical Semantics

arXiv: https://arxiv.org/abs/2212.06629 · HTML: https://mlsanity.com/paper/2212.06629/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 19 references that are in the map.
- First posted on arXiv 13 Dec 2022.

## Builds on (most cited first, 6 of 6)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [From ImageNet to Image Classification: Contextualizing Progress on Benchmarks](https://mlsanity.com/paper/2005.11295/) (2020)
- [Continual egocentric object recognition](https://mlsanity.com/paper/1912.05029/) (2019)
- [Towards Visual Semantics](https://mlsanity.com/paper/2104.12379/) (2021)
- [Object Recognition as Classification via Visual Properties](https://mlsanity.com/paper/2112.10531/) (2021)
- [Visual Ground Truth Construction as Faceted Classification](https://mlsanity.com/paper/2202.08512/) (2022)

## Built on by (most cited first, 7 of 7)

- [A semantics-driven methodology for high-quality image annotation](https://mlsanity.com/paper/2307.14119/) (2023)
- [Incremental Image Labeling via Iterative Refinement](https://mlsanity.com/paper/2304.08989/) (2023)
- [Egocentric Hierarchical Visual Semantics](https://mlsanity.com/paper/2305.05422/) (2023)
- [From Knowledge Representation to Knowledge Organization and Back](https://mlsanity.com/paper/2312.07302/) (2023)
- [From Knowledge Organization to Knowledge Representation and Back](https://mlsanity.com/paper/2401.11753/) (2024)
- [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)

We discuss two kinds of semantics relevant to Computer Vision (CV) systems - Visual Semantics and Lexical Semantics. While visual semantics focus on how humans build concepts when using vision to perceive a target reality, lexical semantics focus on how humans build concepts of the same target reality through the use of language. The lack of coincidence between visual and lexical semantics, in turn, has a major impact on CV systems in the form of the Semantic Gap Problem (SGP). The paper, while extensively exemplifying the lack of coincidence as above, introduces a general, domain-agnostic methodology to enforce alignment between visual and lexical semantics.