File size: 4,394 Bytes
9a10e40 adb5557 9a10e40 c2a75d3 adb5557 c2a75d3 adb5557 9a10e40 c2a75d3 adb5557 c2a75d3 adb5557 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 | ---
language:
- en
license: cc-by-4.0
task_categories:
- image-to-text
- image-text-to-text
pretty_name: ECA-ToS-Benchmarks
tags:
- continual-learning
- vision-language
- image-captioning
- visual-question-answering
- open-ended-image-to-text-generation
- topic-split
- tos
---
# ECA-ToS-Benchmarks
This dataset provides the ToS benchmark annotations used in the paper **[ECA: Efficient Continual Alignment for Open-Ended Image-to-Text Generation](https://huggingface.co/papers/2606.12633)**.
[**Official Code**](https://github.com/Snowball0823/ECA)
ToS is designed for continual learning in open-ended image-to-text generation. Each image is assigned to a task by its dominant visual topic. Other visible topics remain in the sample, so tasks shift over time while shared concepts can still reappear across tasks.
## Dataset Contents
This repository contains annotation files and topic metadata for four benchmarks.
| Benchmark | Source dataset | Task | Annotation files |
| --- | --- | --- | --- |
| ToS-COCO Caption | MSCOCO Caption | Image Captioning | `annotations/coco/tos_coco_caption_*.json` |
| ToS-VQAv2 | VQAv2 | Visual Question Answering | `annotations/coco/tos_vqav2_*.json` |
| ToS-TextCaps | TextCaps | Image Captioning | `annotations/text/tos_textcaps_caption_*.json` |
| ToS-TextVQA | TextVQA | Visual Question Answering | `annotations/text/tos_textvqa_*.json` |
The topic metadata files record the dominant topic and topic composition used to build the splits.
| File | Description |
| --- | --- |
| `annotations/coco/tos_coco_style_topic_metadata.json` | Topic metadata for COCO-style splits |
| `annotations/text/tos_text_style_topic_metadata.json` | Topic metadata shared by ToS-TextCaps and ToS-TextVQA |
The repository also includes the official VQAv2 validation files required by the VQA evaluator.
```text
annotations/coco/answer_list.json
annotations/coco/v2_OpenEnded_mscoco_val2014_questions.json
annotations/coco/v2_mscoco_val2014_annotations.json
```
## Images
Images are not redistributed in this dataset repository.
Please download images from the original dataset sources and place them in the layout expected by the ECA codebase.
```text
cache/coco/images/
cache/TextCaps/images/
```
COCO-style benchmarks use MSCOCO 2014 train and validation images. Text-style benchmarks use the TextCaps and TextVQA image folders.
## Using With The ECA Codebase
Download this dataset and copy the annotation files into the local `cache/` directory.
```bash
huggingface-cli download Snowball0823/ECA-ToS-Benchmarks --repo-type dataset --local-dir data/ECA-ToS-Benchmarks --local-dir-use-symlinks False
mkdir -p cache/coco/annotations cache/TextCaps
cp data/ECA-ToS-Benchmarks/annotations/coco/*.json cache/coco/annotations/
cp data/ECA-ToS-Benchmarks/annotations/text/*.json cache/TextCaps/
```
After copying, the expected runtime layout is:
```text
cache/
coco/
annotations/
tos_coco_caption_train.json
tos_coco_caption_val.json
tos_coco_caption_test.json
tos_vqav2_train.json
tos_vqav2_val_eval.json
tos_coco_style_topic_metadata.json
answer_list.json
v2_OpenEnded_mscoco_val2014_questions.json
v2_mscoco_val2014_annotations.json
images/
train2014/
val2014/
TextCaps/
tos_textcaps_caption_train.json
tos_textcaps_caption_val.json
tos_textcaps_caption_val_eval.json
tos_textvqa_train.json
tos_textvqa_val.json
tos_textvqa_val_eval.json
tos_text_style_topic_metadata.json
images/
train/
test/
```
## License And Source Datasets
This dataset is released under CC BY 4.0. The annotations are derived from MSCOCO Caption, VQAv2, TextCaps, and TextVQA. The original images and annotations remain subject to the licenses and terms of their source datasets.
Users should also cite and follow the original dataset sources.
- COCO: https://cocodataset.org/
- VQAv2: https://visualqa.org/
- TextVQA and TextCaps: https://textvqa.org/
## Citation
If you use this dataset, please cite our paper.
```bibtex
@inproceedings{kong2026eca,
title={ECA: Efficient Continual Alignment for Open-Ended Image-to-Text Generation},
author={Kong, Jiangtao and Zhao, Peijun and Chen, Chun-Fu and Do, Youngwook and Hu, Shaohan and Zhou, Tianyi and Shao, Huajie},
booktitle={International Conference on Machine Learning},
year={2026}
}
``` |