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--- |
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license: apache-2.0 |
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language: |
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- en |
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pipeline_tag: zero-shot-image-classification |
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tags: |
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- image recognition |
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--- |
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# Recognize Anything & Tag2Text |
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Model card for <a href="https://recognize-anything.github.io/">Recognize Anything Plus Model (RAM++) </a>. |
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RAM++ is the next generation of RAM, which can recognize any category with high accuracy, including both predefined common categories and diverse open-set categories. |
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RAM++ outperforms existing SOTA image fundamental reocngition models in terms of common tag categorie, uncommon tag categories, and human-object interaction phrase. |
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## TL;DR |
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Authors from the [paper](https://arxiv.org/abs/2306.03514) write in the abstract: |
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*We introduce the Recognize Anything Plus Model (RAM++), a fundamental image recognition model with strong open-set recognition capabilities, by injecting semantic concepts into image tagging training framework. Previous approaches are either image tagging models constrained by limited semantics, or vision-language models with shallow interaction for suboptimal performance in multi-tag recognition. In contrast, RAM++ integrates image-text alignment and image-tagging within a unified fine-grained interaction framework based on image-tags-text triplets. This design enables RAM++ not only excel in identifying predefined categories, but also significantly augment the recognition ability in open-set categories. Moreover, RAM++ employs large language models~(LLMs) to generate diverse visual tag descriptions, pioneering the integration of LLM's knowledge into image tagging training. This approach empowers RAM++ to integrate visual description concepts for open-set recognition during inference. Evaluations on comprehensive image recognition benchmarks demonstrate RAM++ exceeds existing state-of-the-art (SOTA) fundamental image recognition models on most aspects. * |
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## BibTex and citation info |
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``` |
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@article{zhang2023recognize, |
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title={Recognize Anything: A Strong Image Tagging Model}, |
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author={Zhang, Youcai and Huang, Xinyu and Ma, Jinyu and Li, Zhaoyang and Luo, Zhaochuan and Xie, Yanchun and Qin, Yuzhuo and Luo, Tong and Li, Yaqian and Liu, Shilong and others}, |
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journal={arXiv preprint arXiv:2306.03514}, |
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year={2023} |
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} |
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@article{huang2023tag2text, |
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title={Tag2Text: Guiding Vision-Language Model via Image Tagging}, |
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author={Huang, Xinyu and Zhang, Youcai and Ma, Jinyu and Tian, Weiwei and Feng, Rui and Zhang, Yuejie and Li, Yaqian and Guo, Yandong and Zhang, Lei}, |
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journal={arXiv preprint arXiv:2303.05657}, |
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year={2023} |
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} |
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``` |
