Summary of Jina Clip: Your Clip Model Is Also Your Text Retriever, by Andreas Koukounas et al.
Jina CLIP: Your CLIP Model Is Also Your Text Retriever
by Andreas Koukounas, Georgios Mastrapas, Michael Günther, Bo Wang, Scott Martens, Isabelle Mohr, Saba Sturua, Mohammad Kalim Akram, Joan Fontanals Martínez, Saahil Ognawala, Susana Guzman, Maximilian Werk, Nan Wang, Han Xiao
First submitted to arxiv on: 30 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Information Retrieval (cs.IR)
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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 The proposed multi-task contrastive training method aims to improve the efficiency of information retrieval systems by developing a single CLIP model that can excel in both text-image and text-text retrieval tasks. Building upon the widely used Contrastive Language-Image Pretraining (CLIP) approach, this novel method trains a jina-clip-v1 model to achieve state-of-the-art performance on both multimodal and text-only tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The idea is simple: instead of having separate models and embeddings for different tasks, we can train one CLIP model to handle all tasks efficiently. By leveraging the strengths of both text-image and text-text retrieval tasks, this approach could lead to significant improvements in information retrieval systems and related applications. |
Keywords
» Artificial intelligence » Multi task » Pretraining
