ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2503.17408
39
0

Leveraging OpenFlamingo for Multimodal Embedding Analysis of C2C Car Parts Data

20 March 2025
Maisha Binte Rashid
Pablo Rivas
ArXivPDFHTML
Abstract

In this paper, we aim to investigate the capabilities of multimodal machine learning models, particularly the OpenFlamingo model, in processing a large-scale dataset of consumer-to-consumer (C2C) online posts related to car parts. We have collected data from two platforms, OfferUp and Craigslist, resulting in a dataset of over 1.2 million posts with their corresponding images. The OpenFlamingo model was used to extract embeddings for the text and image of each post. We used kkk-means clustering on the joint embeddings to identify underlying patterns and commonalities among the posts. We have found that most clusters contain a pattern, but some clusters showed no internal patterns. The results provide insight into the fact that OpenFlamingo can be used for finding patterns in large datasets but needs some modification in the architecture according to the dataset.

View on arXiv
@article{rashid2025_2503.17408,
  title={ Leveraging OpenFlamingo for Multimodal Embedding Analysis of C2C Car Parts Data },
  author={ Maisha Binte Rashid and Pablo Rivas },
  journal={arXiv preprint arXiv:2503.17408},
  year={ 2025 }
}
Comments on this paper