Summary of Hyperclip: Adapting Vision-language Models with Hypernetworks, by Victor Akinwande et al.
HyperCLIP: Adapting Vision-Language models with Hypernetworks
by Victor Akinwande, Mohammad Sadegh Norouzzadeh, Devin Willmott, Anna Bair, Madan Ravi Ganesh, J. Zico Kolter
First submitted to arxiv on: 21 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 paper proposes a novel vision-language architecture called HyperCLIP, which addresses the challenge of deploying large-scale vision-language models in resource-constrained environments. By using a small image encoder and a hypernetwork that dynamically adapts weights to each text input, HyperCLIP achieves significant improvements in zero-shot accuracy on ImageNet (up to 3%) and CIFAR-100 (up to 5%). The model is pre-trained end-to-end with joint training of all components, allowing for efficient deployment and task-specific image classification. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you have a super smart AI that can look at pictures and understand what’s in them. But right now, these AIs need really powerful computers to work well. This paper shows how we can make these AIs smaller and more efficient, so they can be used on any device with an internet connection. The new architecture is called HyperCLIP, and it uses a clever trick to adjust the AI’s understanding of images based on what text it sees. This makes the AI way better at recognizing objects in pictures, without needing as much computer power. |
Keywords
» Artificial intelligence » Encoder » Image classification » Zero shot
