Summary of From Latent to Engine Manifolds: Analyzing Imagebind’s Multimodal Embedding Space, by Andrew Hamara and Pablo Rivas
From Latent to Engine Manifolds: Analyzing ImageBind’s Multimodal Embedding Space
by Andrew Hamara, Pablo Rivas
First submitted to arxiv on: 30 Aug 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 This study explores how ImageBind generates meaningful fused multimodal embeddings for online auto parts listings. The researchers propose a simple embedding fusion workflow to capture overlapping information between image/text pairs, creating a joint embedding that combines the semantics of a post. They store these fused embeddings in a vector database and experiment with dimensionality reduction. By clustering and examining the posts nearest to each cluster centroid, they provide empirical evidence for the semantic quality of the joint embeddings. The study also finds initial success with zero-shot cross-modal retrieval, suggesting potential avenues for future research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study is about using a special tool called ImageBind to combine information from images and text on online marketplaces. The researchers want to see if they can create a single “embeddings” that combines the meanings of posts into something useful. They try a simple way to do this and store it in a database. Then, they test it by grouping similar posts together. This helps them understand how well the combined information works. It also shows that audio-only information from marketplace listings can match semantically similar posts, which could lead to new discoveries. |
Keywords
» Artificial intelligence » Clustering » Dimensionality reduction » Embedding » Semantics » Zero shot
