Summary of Clip-moe: Towards Building Mixture Of Experts For Clip with Diversified Multiplet Upcycling, by Jihai Zhang et al.
CLIP-MoE: Towards Building Mixture of Experts for CLIP with Diversified Multiplet Upcycling
by Jihai Zhang, Xiaoye Qu, Tong Zhu, Yu Cheng
First submitted to arxiv on: 28 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 Diversified Multiplet Upcycling (DMU) strategy is a model-agnostic approach that fine-tunes multiple Contrastive Language-Image Pre-training (CLIP) models, capturing different feature spaces from a pre-trained CLIP checkpoint. This method efficiently transforms these models into a CLIP-MoE with enhanced performance and minimal computational overhead. The DMU approach outperforms previous methods in zero-shot retrieval, image classification tasks, and Multimodal Large Language Model (MLLM) benchmarks, serving as a vision encoder. Additionally, DMU enables the conversion of any dense CLIP model into CLIP-MoEs, allowing seamless replacement without further adaptation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way to improve Contrastive Language-Image Pre-training (CLIP) models. CLIP is important for things like recognizing objects in pictures and understanding what’s happening in videos. But the current method has some problems – it can’t capture fine details in images. The new approach, called Diversified Multiplet Upcycling (DMU), makes multiple versions of the original model that are better at capturing different types of information. This helps CLIP models work better for tasks like recognizing objects and understanding videos. |
Keywords
» Artificial intelligence » Encoder » Image classification » Large language model » Zero shot
