# Enhancing Multimodal Understanding with CLIP-Based Image-to-Text Transformation

arXiv: https://arxiv.org/abs/2401.06167 · HTML: https://mlsanity.com/paper/2401.06167/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 17 references that are in the map.
- First posted on arXiv 2 Jan 2024.

## Builds on (most cited first, 10 of 10)

- [ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks](https://mlsanity.com/paper/1908.02265/) (2019)
- [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://mlsanity.com/paper/1511.06434/) (2015)
- [CIDEr: Consensus-based Image Description Evaluation](https://mlsanity.com/paper/1411.5726/) (2014)
- [Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet](https://mlsanity.com/paper/2101.11986/) (2021)
- [Deep Visual-Semantic Alignments for Generating Image Descriptions](https://mlsanity.com/paper/1412.2306/) (2014)
- [Self-critical Sequence Training for Image Captioning](https://mlsanity.com/paper/1612.00563/) (2016)
- [Zero-Shot Learning -- The Good, the Bad and the Ugly](https://mlsanity.com/paper/1703.04394/) (2017)
- [Multi30K: Multilingual English-German Image Descriptions](https://mlsanity.com/paper/1605.00459/) (2016)
- [DenseCap: Fully Convolutional Localization Networks for Dense Captioning](https://mlsanity.com/paper/1511.07571/) (2015)
- [Stack-Captioning: Coarse-to-Fine Learning for Image Captioning](https://mlsanity.com/paper/1709.03376/) (2017)

## Built on by (most cited first, 15 of 15)

- [SwitchTab: Switched Autoencoders Are Effective Tabular Learners](https://mlsanity.com/paper/2401.02013/) (2024)
- [SmartCLIP: Modular Vision-language Alignment with Identification Guarantees](https://mlsanity.com/paper/2507.22264/) (2025)
- [Enhancing Multi-Hop Knowledge Graph Reasoning through Reward Shaping Techniques](https://mlsanity.com/paper/2403.05801/) (2024)
- [When is an Embedding Model More Promising than Another?](https://mlsanity.com/paper/2406.07640/) (2024)
- [Multi-modal Summarization in Model-Based Engineering: Automotive Software Development Case Study](https://mlsanity.com/paper/2503.04506/) (2025)
- [Progress in artificial intelligence applications based on the combination of self-driven sensors and deep learning](https://mlsanity.com/paper/2402.09442/) (2024)
- [Leveraging Deep Learning and Xception Architecture for High-Accuracy MRI Classification in Alzheimer Diagnosis](https://mlsanity.com/paper/2403.16212/) (2024)
- [AEye: A Visualization Tool for Image Datasets](https://mlsanity.com/paper/2408.04072/) (2024)
- [LSDM: LLM-Enhanced Spatio-temporal Diffusion Model for Service-Level Mobile Traffic Prediction](https://mlsanity.com/paper/2507.17795/) (2025)
- [Enhancing Multimodal Retrieval via Complementary Information Extraction and Alignment](https://mlsanity.com/paper/2601.04571/) (2026)
- [The intelligent prediction and assessment of financial information risk in the cloud computing model](https://mlsanity.com/paper/2404.09322/) (2024)
- [An Iterative Feedback Mechanism for Improving Natural Language Class Descriptions in Open-Vocabulary Object Detection](https://mlsanity.com/paper/2503.17285/) (2025)
- [Visually grounded emotion regulation via diffusion models and user-driven reappraisal](https://mlsanity.com/paper/2507.10861/) (2025)
- [Checking Fact with Better Retrieval: Dynamic Contrastive Learning for Evidence Retrieval](https://mlsanity.com/paper/2605.27449/) (2026)
- [Image Augmentation as Test Generation for Deep Learning-Based Image Retrieval Systems](https://mlsanity.com/paper/2608.27502/) (2026)

## Abstract (arXiv)

The process of transforming input images into corresponding textual explanations stands as a crucial and complex endeavor within the domains of computer vision and natural language processing. In this paper, we propose an innovative ensemble approach that harnesses the capabilities of Contrastive Language-Image Pretraining models.