Zero-shot Text-to-Image Retrieval
4 papers with code • 1 benchmarks • 2 datasets
Benchmarks
These leaderboards are used to track progress in Zero-shot Text-to-Image Retrieval
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
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COCO-CN
FewSOL
Most implemented papers
ZSCRGAN: A GAN-based Expectation Maximization Model for Zero-Shot Retrieval of Images from Textual Descriptions
ranarag/ZSCRGAN
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Most existing algorithms for cross-modal Information Retrieval are based on a supervised train-test setup, where a model learns to align the mode of the query (e. g., text) to the mode of the documents (e. g., images) from a given training set.
ERNIE-ViL 2.0: Multi-view Contrastive Learning for Image-Text Pre-training
They attempt to learn cross-modal representation using contrastive learning on image-text pairs, however, the built inter-modal correlations only rely on a single view for each modality.
Chinese CLIP: Contrastive Vision-Language Pretraining in Chinese
The tremendous success of CLIP (Radford et al., 2021) has promoted the research and application of contrastive learning for vision-language pretraining.
M2-Encoder: Advancing Bilingual Image-Text Understanding by Large-scale Efficient Pretraining
alipay/Ant-Multi-Modal-Framework
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Vision-language foundation models like CLIP have revolutionized the field of artificial intelligence.
