Recall@1
A commonly used evaluation metric in retrieval tasks that measures the probability that the most similar sample returned by a model is ranked first, i.e., that it is the correct pairing.
Timeline (last 90 days)
Recall@1 is one of the most commonly used evaluation metrics in image retrieval, referring to the proportion of queries whose correct matching sample is hit among the candidates ranked first in the retrieval results.
The evaluation metrics include raw Recall@1 (measuring direct retrieval capability) and paired token cosine similarity (measuring the consistency of latent coordinates).
The experimental results show that even with identical inputs, the raw Recall@1 between latent representations produced by different schemas is only 5.9%–17.7%.
Using only 64 paired segments, raw Recall@1 can be raised to 88.4%–90.3%, and token cosine similarity to about 0.82.
All Facts (4)
Recall@1 is one of the most commonly used evaluation metrics in image retrieval, referring to the proportion of queries whose correct matching sample is hit among the candidates ranked first in the retrieval results.
50%UnverifiedThe evaluation metrics include raw Recall@1 (measuring direct retrieval capability) and paired token cosine similarity (measuring the consistency of latent coordinates).
50%UnverifiedThe experimental results show that even with identical inputs, the raw Recall@1 between latent representations produced by different schemas is only 5.9%–17.7%.
50%UnverifiedUsing only 64 paired segments, raw Recall@1 can be raised to 88.4%–90.3%, and token cosine similarity to about 0.82.
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