115 lines
5.2 KiB
Markdown
115 lines
5.2 KiB
Markdown
<h1 align="center">Vis-IR: Unifying Search With Visualized Information Retrieval</h1>
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<p align="center">
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<a href="https://arxiv.org/abs/2502.11431">
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<img alt="Build" src="http://img.shields.io/badge/arXiv-2502.11431-B31B1B.svg">
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</a>
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<a href="https://github.com/VectorSpaceLab/Vis-IR">
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<img alt="Build" src="https://img.shields.io/badge/Github-Code-blue">
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</a>
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<a href="https://huggingface.co/datasets/marsh123/VIRA/">
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<img alt="Build" src="https://img.shields.io/badge/🤗 Datasets-VIRA-yellow">
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</a>
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<a href="https://huggingface.co/datasets/marsh123/MVRB">
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<img alt="Build" src="https://img.shields.io/badge/🤗 Datasets-MVRB-yellow">
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</a>
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<!-- <a href="">
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<img alt="Build" src="https://img.shields.io/badge/🤗 Model-UniSE CLIP-yellow">
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</a> -->
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<a href="https://huggingface.co/marsh123/UniSE">
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<img alt="Build" src="https://img.shields.io/badge/🤗 Model-UniSE MLLM-yellow">
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</a>
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</p>
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<h4 align="center">
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<p>
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<a href=#news>News</a> |
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<a href=#release-plan>Release Plan</a> |
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<a href=#overview>Overview</a> |
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<a href="#license">License</a> |
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<a href="#citation">Citation</a>
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<p>
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</h4>
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## News
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```2025-04-06``` 🚀🚀 MVRB Dataset are released on Huggingface: [MVRB](https://huggingface.co/datasets/marsh123/MVRB)
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```2025-04-02``` 🚀🚀 VIRA Dataset are released on Huggingface: [VIRA](https://huggingface.co/datasets/marsh123/VIRA/)
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```2025-04-01``` 🚀🚀 UniSE models are released on Huggingface: [UniSE-MLMM](https://huggingface.co/marsh123/UniSE-MLLM/)
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```2025-02-17``` 🎉🎉 Release our paper: [Any Information Is Just Worth One Single Screenshot: Unifying Search With Visualized Information Retrieval](https://arxiv.org/abs/2502.11431).
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## Release Plan
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- [x] Paper
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- [x] UniSE models
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- [x] VIRA Dataset
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- [x] MVRB benchmark
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- [ ] Evaluation code
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- [ ] Fine-tuning code
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## Overview
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In this work, we formally define an emerging IR paradigm called Visualized Information Retrieval, or **VisIR**, where multimodal information, such as texts, images, tables and charts, is jointly represented by a unified visual format called **Screenshots**, for various retrieval applications. We further make three key contributions for VisIR. First, we create **VIRA** (Vis-IR Aggregation), a large-scale dataset comprising a vast collection of screenshots from diverse sources, carefully curated into captioned and questionanswer formats. Second, we develop **UniSE** (Universal Screenshot Embeddings), a family of retrieval models that enable screenshots to query or be queried across arbitrary data modalities. Finally, we construct **MVRB** (Massive Visualized IR Benchmark), a comprehensive benchmark covering a variety of task forms and application scenarios. Through extensive evaluations on MVRB, we highlight the deficiency from existing multimodal retrievers and the substantial improvements made by UniSE.
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## Model Usage
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> Our code works well on transformers==4.45.2, and we recommend using this version.
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### 1. UniSE-MLLM Models
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```python
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import torch
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from transformers import AutoModel
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MODEL_NAME = "marsh123/UniSE-MLLM"
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model = AutoModel.from_pretrained(MODEL_NAME, trust_remote_code=True)
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# You must set trust_remote_code=True
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model.set_processor(MODEL_NAME)
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with torch.no_grad():
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device = torch.device("cuda:0")
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model = model.to(device)
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model.eval()
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query_inputs = model.data_process(
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images=["./assets/query_1.png", "./assets/query_2.png"],
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text=["After a 17% drop, what is Nvidia's closing stock price?",
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"I would like to see a detailed and intuitive performance comparison between the two models."],
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q_or_c="query",
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task_instruction="Represent the given image with the given query."
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)
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candidate_inputs = model.data_process(
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images=["./assets/positive_1.jpeg", "./assets/neg_1.jpeg",
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"./assets/positive_2.jpeg", "./assets/neg_2.jpeg"],
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q_or_c="candidate"
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)
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query_embeddings = model(**query_inputs)
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candidate_embeddings = model(**candidate_inputs)
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scores = torch.matmul(query_embeddings, candidate_embeddings.T)
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print(scores)
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```
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## Performance on MVRB
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MVRB is a comprehensive benchmark designed for the retrieval task centered on screenshots. It includes four meta tasks: Screenshot Retrieval (SR), Composed Screenshot Retrieval (CSR), Screenshot QA (SQA), and Open-Vocabulary Classification (OVC). We evaluate three main types of retrievers on MVRB: OCR+Text Retrievers, General Multimodal Retrievers, and Screenshot Document Retrievers. Our proposed UniSE-MLLM achieves state-of-the-art (SOTA) performance on this benchmark.
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## License
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Vis-IR is licensed under the [MIT License](LICENSE).
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## Citation
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If you find this repository useful, please consider giving a star ⭐ and citation
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```
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@article{liu2025any,
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title={Any Information Is Just Worth One Single Screenshot: Unifying Search With Visualized Information Retrieval},
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author={Liu, Ze and Liang, Zhengyang and Zhou, Junjie and Liu, Zheng and Lian, Defu},
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journal={arXiv preprint arXiv:2502.11431},
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year={2025}
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}
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```
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