Summary of Enclip: Ensembling and Clustering-based Contrastive Language-image Pretraining For Fashion Multimodal Search with Limited Data and Low-quality Images, by Prithviraj Purushottam Naik et al.
ENCLIP: Ensembling and Clustering-Based Contrastive Language-Image Pretraining for Fashion Multimodal Search with Limited Data and Low-Quality Images
by Prithviraj Purushottam Naik, Rohit Agarwal
First submitted to arxiv on: 25 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Multimedia (cs.MM)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary ENCLIP, an innovative approach, enhances the performance of the Contrastive Language-Image Pretraining (CLIP) model in Multimodal Search targeting fashion intelligence. The method addresses challenges posed by limited data availability and low-quality images. ENCLIP trains and ensembles multiple CLIP instances, leveraging clustering techniques to group similar images together. Experimental findings demonstrate the effectiveness of this methodology, unlocking the potential of CLIP for optimizing model performance in scenarios with limited data and low-quality images. The paper focuses on enhancing the performance of CLIP in multimodal search, particularly in the fashion intelligence domain where data scarcity and image quality issues are prevalent. The proposed algorithm involves training multiple instances of the CLIP model and using clustering techniques to group similar images together. This approach can be applied to optimize the CLIP model in various scenarios with limited data and low-quality images. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper presents a new way to improve the performance of a computer model called CLIP. CLIP helps people search for fashion items by combining text and images. However, sometimes there isn’t enough information or the images are poor quality. The researchers developed an algorithm called ENCLIP to solve these problems. They trained multiple versions of the CLIP model and grouped similar images together. This made the model work better in situations where data is limited and image quality is poor. The goal was to make CLIP more effective for searching fashion items, which often have limited data and low-quality images. The new algorithm can be used to improve CLIP’s performance in various scenarios. |
Keywords
» Artificial intelligence » Clustering » Pretraining
