# From Latent to Engine Manifolds: Analyzing ImageBind's Multimodal Embedding Space

arXiv: https://arxiv.org/abs/2409.10528 · HTML: https://mlsanity.com/paper/2409.10528/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 33 references that are in the map.
- First posted on arXiv 30 Aug 2024.

## Builds on (most cited first, 19 of 19)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [Flamingo: a Visual Language Model for Few-Shot Learning](https://mlsanity.com/paper/2204.14198/) (2022)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [ImageBind: One Embedding Space To Bind Them All](https://mlsanity.com/paper/2305.05665/) (2023)
- [Chameleon: Mixed-Modal Early-Fusion Foundation Models](https://mlsanity.com/paper/2405.09818/) (2024)
- [Contrastive Learning of Medical Visual Representations from Paired Images and Text](https://mlsanity.com/paper/2010.00747/) (2020)
- [AudioCLIP: Extending CLIP to Image, Text and Audio](https://mlsanity.com/paper/2106.13043/) (2021)
- [A Semi-Personalized System for User Cold Start Recommendation on Music Streaming Apps](https://mlsanity.com/paper/2106.03819/) (2021)
- [Frustratingly Easy Meta-Embedding -- Computing Meta-Embeddings by Averaging Source Word Embeddings](https://mlsanity.com/paper/1804.05262/) (2018)
- [Representation learning for very short texts using weighted word embedding aggregation](https://mlsanity.com/paper/1607.00570/) (2016)
- [How much does a word weigh? Weighting word embeddings for word sense induction](https://mlsanity.com/paper/1805.09209/) (2018)
- [Supervising Unsupervised Learning](https://mlsanity.com/paper/1709.05262/) (2017)

## Built on by (most cited first, 1 of 1)

- [Exploring Visual Embedding Spaces Induced by Vision Transformers for Online Auto Parts Marketplaces](https://mlsanity.com/paper/2502.05756/) (2025)

## Abstract (arXiv)

This study investigates ImageBind's ability to generate meaningful fused multimodal embeddings for online auto parts listings. We propose a simplistic embedding fusion workflow that aims to capture the overlapping information of image/text pairs, ultimately combining the semantics of a post into a joint embedding. After storing such fused embeddings in a vector database, we experiment with dimensionality reduction and provide empirical evidence to convey the semantic quality of the joint embeddings by clustering and examining the posts nearest to each cluster centroid. Additionally, our initial findings with ImageBind's emergent zero-shot cross-modal retrieval suggest that pure audio embeddings can correlate with semantically similar marketplace listings, indicating potential avenues for future research.