Summary of Optimizing Clip Models For Image Retrieval with Maintained Joint-embedding Alignment, by Konstantin Schall et al.
Optimizing CLIP Models for Image Retrieval with Maintained Joint-Embedding Alignment
by Konstantin Schall, Kai Uwe Barthel, Nico Hezel, Klaus Jung
First submitted to arxiv on: 3 Sep 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 two novel methods to optimize Contrastive Language and Image Pairing (CLIP) models for various image-based similarity search scenarios while maintaining their effectiveness in text-based search tasks. The first method involves a sequential fine-tuning process, initially optimizing the image encoder and subsequently realigning the text encoder. The second approach integrates pseudo-captions during the retrieval-optimization phase to foster direct alignment within the embedding space. Experimental results show that these methods enhance CLIP’s performance on various benchmarks, including image retrieval, k-NN classification, and zero-shot text-based classification, while maintaining robustness in text-to-image retrieval. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper tries to make a computer program called CLIP better at finding similar images when given some words. Right now, CLIP has trouble telling apart pictures that look different but have the same description. The researchers want to fix this problem so they can use CLIP for things like searching for images on the internet or classifying pictures without needing more training data. They come up with two new ways to improve CLIP: one is a step-by-step process that makes the image-part of CLIP better, then aligns the text-part again. The other way adds fake captions during training to help CLIP understand how images and words match up. They test these new methods on different tasks like finding similar pictures or classifying text without training data, and it works better than before! |
Keywords
» Artificial intelligence » Alignment » Classification » Embedding space » Encoder » Fine tuning » Optimization » Zero shot
