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Batik image captioning improved with class-grounded AI under scarce annotation

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a method for generating captions for batik images, particularly when text annotations are scarce. The approach uses a DINOv2 classifier adapted with LoRA to identify classes, which then informs the Qwen2.5-VL-7B captioning model. This class-grounded method successfully retained label information in captions, achieving a high ground-truth class-label mention rate. However, the study also highlighted the propagation of classification errors into the generated descriptions, indicating a need for further refinement to address upstream errors. AI

IMPACT This research offers a method to improve image captioning in domains with limited text data, potentially benefiting cultural heritage preservation and specialized AI applications.

RANK_REASON Academic paper detailing a novel method for image captioning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Batik image captioning improved with class-grounded AI under scarce annotation

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Academic paper detailing a novel method for image captioning. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Novanto Yudistira, Muhammad Dzaka Aufa Fadhillah, Irawati Nurmala Sari ·

    Class-Grounded Batik Captioning under Scarce Text Annotation: Label Retention and Source-Specific Error Propagation

    arXiv:2610.30661v1 Announce Type: new Abstract: Class labels offer compact supervision for describing cultural-heritage images when expert text annotations are scarce. We investigate a batik-captioning cascade that integrates a LoRA-adapted DINOv2 classifier with Qwen2.5-VL-7B, a…

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