Recognize Anything: A Strong Image Tagging Model
We present the Recognize Anything Model (RAM): a strong foundation model for image tagging. RAM makes a substantial step for large models in computer vision, demonstrating the zero-shot ability to recognize any common category with high accuracy. RAM introduces a new paradigm for image tagging, leve...
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Zusammenfassung: | We present the Recognize Anything Model (RAM): a strong foundation model for
image tagging. RAM makes a substantial step for large models in computer
vision, demonstrating the zero-shot ability to recognize any common category
with high accuracy. RAM introduces a new paradigm for image tagging, leveraging
large-scale image-text pairs for training instead of manual annotations.
The development of RAM comprises four key steps. Firstly, annotation-free
image tags are obtained at scale through automatic text semantic parsing.
Subsequently, a preliminary model is trained for automatic annotation by
unifying the caption and tagging tasks, supervised by the original texts and
parsed tags, respectively. Thirdly, a data engine is employed to generate
additional annotations and clean incorrect ones. Lastly, the model is retrained
with the processed data and fine-tuned using a smaller but higher-quality
dataset.
We evaluate the tagging capabilities of RAM on numerous benchmarks and
observe impressive zero-shot performance, significantly outperforming CLIP and
BLIP. Remarkably, RAM even surpasses the fully supervised manners and exhibits
competitive performance with the Google tagging API. We are releasing the RAM
at \url{https://recognize-anything.github.io/} to foster the advancements of
large models in computer vision. |
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DOI: | 10.48550/arxiv.2306.03514 |