OVCOD-D: Open-Vocabulary Camouflaged Object Detection Dataset

An open-vocabulary camouflaged object detection dataset created by researchers from Shenzhen University and The Hong Kong University of Science and Technology (Guangzhou). The dataset is associated with the paper 'SDDF: Specificity-Driven Dynamic Focusing for Open-Vocabulary Camouflaged Object Detection' and was last updated on the Hugging Face platform in April 2026.

Use Cases

• Training open-vocabulary object detection models based on the described focus on camouflaged objects.
• Benchmarking model performance on challenging camouflaged object detection tasks.
• Researching specificity-driven dynamic focusing methods as referenced in the associated paper.

Strengths

• Dataset is directly associated with a published research paper, suggesting a defined research purpose.
• Focus on the specific and challenging computer vision task of detecting camouflaged objects.
• Extends object detection to an open-vocabulary setting, a more flexible paradigm.

Limitations

• Description metadata is limited; actual data quality, size, and format require manual inspection after download.
• Column-level documentation is absent; field semantics must be inferred after download.
• Row count and file formats are unknown, which may limit suitability assessment.

Provenance

Source

Zhan1fen on Hugging Face, associated with academic researchers from Shenzhen University and HKUST(GZ).

Collection Method

Likely created for the research paper 'SDDF: Specificity-Driven Dynamic Focusing for Open-Vocabulary Camouflaged Object Detection'.

Time Range

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Freshness

Last updated 2026-04-13 15:33:25; freshness should be verified.

Geography

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