DQE-CIR: Distinctive Query Embeddings through Learnable Attribute Weights and Target Relative Negative Sampling in Composed Image Retrieval | ScienceToStartup | ScienceToStartup

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References (46)

[1]

DisenQ: Disentangling Q-Former for Activity-Biometrics

2025Shehreen Azad, Y. S. Rawat

[2]

Interactive Image Retrieval Meets Query Rewriting with Large Language and Vision Language Models

2025Hongyi Zhu, Jia-Hong Huang et al.

[3]

Learning with Noisy Triplet Correspondence for Composed Image Retrieval‡

Founder's Pitch

"Develops a method to enhance composed image retrieval by improving query discriminativeness and reducing semantic confusion."

Composed Image Retrieval•Score: 3•View PDF ↗

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DQE-CIR: Distinctive Query Embeddings through Learnable Attribute Weights and Target Relative Negative Sampling in Composed Image Retrieval

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References (46)

Founder's Pitch

"Develops a method to enhance composed image retrieval by improving query discriminativeness and reducing semantic confusion."

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DQE-CIR: Distinctive Query Embeddings through Learnable Attribute Weights and Target Relative Negative Sampling in Composed Image Retrieval

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References (46)

Founder's Pitch

"Develops a method to enhance composed image retrieval by improving query discriminativeness and reducing semantic confusion."

Commercial Viability Breakdown

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