Summary of Spherical Linear Interpolation and Text-anchoring For Zero-shot Composed Image Retrieval, by Young Kyun Jang et al.
Spherical Linear Interpolation and Text-Anchoring for Zero-shot Composed Image Retrieval
by Young Kyun Jang, Dat Huynh, Ashish Shah, Wen-Kai Chen, Ser-Nam Lim
First submitted to arxiv on: 1 May 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 Zero-Shot Composed Image Retrieval (ZS-CIR) method addresses scalability and applicability limitations by introducing novel approaches. A Spherical Linear Interpolation (Slerp) technique merges image and text representations, while Text-Anchored-Tuning (TAT) fine-tunes the image encoder. This combination achieves state-of-the-art performance on CIR benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine having a magic camera that can find pictures of what you want to see! This is called Composed Image Retrieval. Right now, finding these images requires lots of work and special training data. To make it easier, scientists have created ways to use words to help find the right pictures. But this method has some problems. They found a new way to combine image and word ideas together by using something called Slerp. This helps us get better results. They also came up with another trick called TAT that makes it more efficient and accurate. By combining these two ideas, they got even better results than before! |
Keywords
» Artificial intelligence » Encoder » Zero shot
