LLM2CLIP-EVA02-L-14-336
| Property | Value |
|---|---|
| License | Apache 2.0 |
| Paper | arXiv:2411.04997 |
| Training Data | CC3M, CC12M, YFCC15M, Recap-DataComp-1B(30M subset) |
| Primary Task | Zero-Shot Image Classification |
What is LLM2CLIP-EVA02-L-14-336?
LLM2CLIP-EVA02-L-14-336 is an innovative vision foundation model that combines the power of Large Language Models (LLMs) with CLIP architecture to enhance visual representation capabilities. This model represents a significant advancement in cross-modal understanding, achieving a 16.5% performance improvement over the previous state-of-the-art EVA02 model.
Implementation Details
The model employs a sophisticated architecture that fine-tunes LLMs in the caption space using contrastive learning. It extracts textual capabilities into output embeddings, significantly improving textual discriminability. The implementation allows for processing longer and more complex captions, overcoming traditional CLIP text encoder limitations.
- Utilizes PyTorch framework for implementation
- Supports both long-text and short-text retrieval tasks
- Implements efficient training process with LLM as teacher for CLIP's visual encoder
- Features cross-lingual capabilities despite English-only training data
Core Capabilities
- Zero-shot image classification with enhanced accuracy
- Cross-modal understanding between text and images
- Improved performance in multimodal tasks when integrated with models like Llava 1.5
- Superior text-to-image retrieval capabilities
Frequently Asked Questions
Q: What makes this model unique?
This model's uniqueness lies in its ability to leverage LLMs to enhance CLIP's capabilities, resulting in significantly improved performance in cross-modal tasks and the ability to handle longer, more complex captions than traditional CLIP models.
Q: What are the recommended use cases?
The model is particularly well-suited for zero-shot image classification, cross-modal retrieval tasks, and applications requiring sophisticated understanding of both visual and textual content. It excels in scenarios where traditional CLIP models might struggle with complex or lengthy textual descriptions.
