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

Researchers propose DQE-CIR, a new method for composed image retrieval that improves AI's ability to find images based on reference images and text modifications. The approach addresses limitations in current contrastive learning frameworks by using learnable attribute weights and target relative negative sampling to create more distinctive query embeddings.