Factorized MLLM embeddings for composed image retrieval
FactEmbed decouples compositional query comprehension from discriminative embeddings using factorized latents and contrastive losses for improved CIR performance.
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Stories tagged #image-retrieval
FDSML uses formula parameters for metric learning without real images
On ResNet-50, Recall@1 rises from 65.6% to 74.0% on Stanford Online Products and from 56.2% to 78.4% on Stanford Cars versus FDSL.