I remember this from a while back - good to bring back memories. This may be a bit excessive, but using CLIP (https://towhee.io/image-text-embedding/clip) or some other multimodal learning model in conjunction with Milvus (https://milvus.io) could make for a search engine that takes the graphics into consideration as well. Not sure how well it would work for comics, but it could make for a pretty unique weekend hack.
Readit Newsfzliu commented on Calvin and Hobbes Search Engine michaelyingling.com/rando... · Posted by u/Arubis
mrintellectual commented on Content-Based Image Retrieval pinecone.io/learn/color-h... · Posted by u/gk1
If folks just want to get started with vector search faster they can try https://www.pinecone.io.
Full disclosure: I work for Pinecone. It's important to disclose you work for a company if you're going to promote their links.
Good catch on the disclosure, I edited my original comment to reflect this fact.
On the topic of vector search, Milvus is another great vector database - it's open source and we provide single-line startup scripts via `docker-compose` in addition to installation via apt & yum (https://milvus.io/docs/install_standalone-docker.md). There are also no restrictions on the number of vectors that users can store. Internally, we've successfully scaled Milvus to handle billion+ vectors, while many of our users have stored hundreds of millions of vectors in a production environments as well.
mrintellectual commented on Content-Based Image Retrieval pinecone.io/learn/color-h... · Posted by u/gk1
Great article. We used something very similar to help implement simlarity search at Yahoo a couple years back (https://yahooresearch.tumblr.com/post/158115871236/introduci...). We were using a indexing strategy called Locally Optimized Product Quantization, which worked great in terms of query times but required a training procedure which made successive inserts fairly inefficient.
Thankfully, we have a much wider variety of indexing options these days (https://milvus.io/docs/index.md) in addition to powerful vector databases (https://zilliz.com/learn/what-is-vector-database). I'm glad to see the barrier to entry for semantic image retrieval becoming lower and lower as ML infrastructure matures.
[EDIT] Disclosure: I work at Zilliz.
fzliu commented on Similarity Learning lacks a framework. So we built one blog.qdrant.tech/similari... · Posted by u/generall
Great article. I've been working in and around this space since 2014, and I think similarity learning, vector search, and embedding management will be a core part of future applications that leverage ML.
I recently built a similarity search application that recommends new Pinterest users channels to follow based on liked images using Milvus (https://github.com/milvus-io/milvus) as a backend. Similarity learning is a huge part of it, and I'm glad more and more tools like Quaterion are being released to help make this kind of tech ubiquitous.
With that being said, match between Ian and Ding would also be incredibly entertaining. I look forward to it.