Zero-shot Composed Text-Image Retrieval Study
The authors propose a retrieval-based pipeline for automatic dataset construction and introduce TransAgg, a transformer-based adaptive aggregation model, showing competitive performance in zero-shot composed image retrieval.
Zero-shot Composed Text-Image Retrieval Study
Initiating a scalable pipeline for automatic dataset construction and proposing TransAgg model for zero-shot composed image retrieval.
Scaling Positive and Negative Examples to Improve Composed Image Retrieval via Contrastive Learning
Scaling the number of positive and negative examples in contrastive learning can effectively improve the performance of composed image retrieval models.
Effective Raw-Data Level Multimodal Fusion for Composed Image Retrieval
A simple yet effective framework, DQU-CIR, that performs raw-data level multimodal fusion to fully leverage the multimodal encoding and cross-modal alignment capabilities of vision-language pre-trained models for composed image retrieval.
