How to use BLIP for near-duplicate image and text pair detection?

[smith-co]

Given pairs of (image,text), I have to detect near duplicate using both features.

Pairs

(image1, text1)
(image2, text2)
...
(imageN, textN)

I am thinking to compute embedding using both image and text features:

multimodal_feature_1 = model(image1, text1, mode='multimodal')[0,0]
multimodal_feature_2 = model(image2, text2, mode='multimodal')[0,0]
matching_score = cosine_similarity(multimodal_feature_1, multimodal_feature_2)

Any feedback about this approach?

Also I would like to know is there a length limit for text that I should be aware of?

[woctezuma]

Related:

• How to use BLIP for duplicate or near-duplicate images? #68
• How to train BLIP to generate embeddings for new image-text pairs? #69

[LiJunnan1992]

Hi, note that the multimodal feature has not been optimized for cosine-similarity. The unimodal features can be used to compute cosine-similarity because of the image-text contrastive loss.