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This is a Experimental Study study.

October 1, 2025Open Access

Describe Anything: Detailed Localized Image and Video Captioning

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This model generates accurate local descriptions in images and videos, highlighting its advances in semi-supervised learning.

DAM achieves state-of-the-art performance in detailed localized captioning on seven benchmarks, improving context understanding.
The focal prompt enhances high-resolution encoding of targeted regions, allowing for better detail preservation.
The proposed SSL-based Data Pipeline expands training data from existing segmentation datasets to unlabeled web images.
DLC-Bench provides a new metric for evaluating localized captioning independently of reference captions.

Lian et al. (2025) studied this question.

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October 1, 2025Open Access

View Full Paper

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Authors

This model generates accurate local descriptions in images and videos, highlighting its advances in semi-supervised learning.

DAM achieves state-of-the-art performance in detailed localized captioning on seven benchmarks, improving context understanding.
The focal prompt enhances high-resolution encoding of targeted regions, allowing for better detail preservation.
The proposed SSL-based Data Pipeline expands training data from existing segmentation datasets to unlabeled web images.
DLC-Bench provides a new metric for evaluating localized captioning independently of reference captions.

Lian et al. (2025) studied this question.

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