Summary of Zoom-shot: Fast and Efficient Unsupervised Zero-shot Transfer Of Clip to Vision Encoders with Multimodal Loss, by Jordan Shipard et al.
Zoom-shot: Fast and Efficient Unsupervised Zero-Shot Transfer of CLIP to Vision Encoders with Multimodal Loss
by Jordan Shipard, Arnold Wiliem, Kien Nguyen Thanh, Wei Xiang, Clinton Fookes
First submitted to arxiv on: 22 Jan 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 fusion of vision and language has revolutionized computer vision through Vision-Language Models (VLMs). However, the resource-intensive nature of existing VLMs poses a significant challenge. This paper proposes Zoom-shot, a novel method for transferring the zero-shot capabilities of CLIP to any pre-trained vision encoder. By exploiting multimodal information in the CLIP latent space using specifically designed loss functions, Zoom-shot trains a linear mapping between the CLIP and vision encoder’s latent spaces for only one epoch. The unsupervised and unpaired data training allows for a trade-off between data and compute during training. This paper outperforms the previous state-of-the-art in zero-shot classification on coarse and fine-grained datasets, achieving impressive results even with reduced training data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to make computer vision better by combining it with language. It’s like having a superpower that lets you understand pictures without needing lots of examples. The method they use is called Zoom-shot, and it helps transfer the power from one type of model to another. They do this by using special tricks with words and images to help the new model learn quickly. This new way of training models saves time and energy while still getting great results. It’s a big deal for people working on computer vision because it makes their job easier and faster. |
Keywords
» Artificial intelligence » Classification » Encoder » Latent space » Unsupervised » Zero shot
