Image-to-Text Retrieval
Image-text retrieval is the process of retrieving relevant images based on textual descriptions or finding corresponding textual descriptions for a given image. This task is interdisciplinary, combining techniques from computer vision, and natural language processing. The primary challenge lies in bridging the semantic gap — the difference between how visual data is represented in images and how humans describe that information using language. To address this, many methods focus on learning a shared embedding space where both images and text can be represented in a comparable way, allowing their similarities to be measured and facilitating more accurate retrieval.
Source: Extending CLIP for Category-to-Image Retrieval in E-commerce
Papers
Showing 1–10 of 59 papers
Benchmark Results
| # | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| 1 | Oscar | Recall@10 | 99.8 | — | Unverified |
| 2 | Oscar | Recall@10 | 98.3 | — | Unverified |
| 3 | Unicoder-VL | Recall@10 | 97.2 | — | Unverified |
| 4 | BLIP-2 (ViT-G, fine-tuned) | Recall@1 | 85.4 | — | Unverified |
| 5 | ONE-PEACE (ViT-G, w/o ranking) | Recall@1 | 84.1 | — | Unverified |
| 6 | BLIP-2 (ViT-L, fine-tuned) | Recall@1 | 83.5 | — | Unverified |
| 7 | DVSA | Recall@10 | 74.8 | — | Unverified |
| 8 | IAIS | Recall@1 | 67.78 | — | Unverified |
| 9 | CLIP (zero-shot) | Recall@1 | 58.4 | — | Unverified |
| 10 | FLAVA (ViT-B, zero-shot) | Recall@1 | 42.74 | — | Unverified |