Saved in:
| Main Authors: | , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.20564 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866916867134717952 |
|---|---|
| author | Dinh, Duc-Tai Dinh, Duc Anh Khoa |
| author_facet | Dinh, Duc-Tai Dinh, Duc Anh Khoa |
| contents | We present ZSE-Cap (Zero-Shot Ensemble for Captioning), our 4th place system in Event-Enriched Image Analysis (EVENTA) shared task on article-grounded image retrieval and captioning. Our zero-shot approach requires no finetuning on the competition's data. For retrieval, we ensemble similarity scores from CLIP, SigLIP, and DINOv2. For captioning, we leverage a carefully engineered prompt to guide the Gemma 3 model, enabling it to link high-level events from the article to the visual content in the image. Our system achieved a final score of 0.42002, securing a top-4 position on the private test set, demonstrating the effectiveness of combining foundation models through ensembling and prompting. Our code is available at https://github.com/ductai05/ZSE-Cap. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_20564 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ZSE-Cap: A Zero-Shot Ensemble for Image Retrieval and Prompt-Guided Captioning Dinh, Duc-Tai Dinh, Duc Anh Khoa Computation and Language Information Retrieval We present ZSE-Cap (Zero-Shot Ensemble for Captioning), our 4th place system in Event-Enriched Image Analysis (EVENTA) shared task on article-grounded image retrieval and captioning. Our zero-shot approach requires no finetuning on the competition's data. For retrieval, we ensemble similarity scores from CLIP, SigLIP, and DINOv2. For captioning, we leverage a carefully engineered prompt to guide the Gemma 3 model, enabling it to link high-level events from the article to the visual content in the image. Our system achieved a final score of 0.42002, securing a top-4 position on the private test set, demonstrating the effectiveness of combining foundation models through ensembling and prompting. Our code is available at https://github.com/ductai05/ZSE-Cap. |
| title | ZSE-Cap: A Zero-Shot Ensemble for Image Retrieval and Prompt-Guided Captioning |
| topic | Computation and Language Information Retrieval |
| url | https://arxiv.org/abs/2507.20564 |