research
The Mosquito's Flight Carried an Infection Signal
A vision-language pipeline separated DENV2-infected and uninfected mosquito video in the authors' controlled dataset.
Summary
A vision-language pipeline separated DENV2-infected and uninfected mosquito video in the authors' controlled dataset.
The system first isolates mosquito regions with an object detector, then aligns visual features with biologically meaningful text prompts. The paper reports 98.54 percent frame accuracy and 99.91 percent sensitivity, with complete video-level performance after temporal aggregation. An ablation found that fine-tuning and visual representations drove accuracy while language supplied semantic alignment; the study is a dataset result, not a clinical or field diagnostic validation.
Why it matters
A vision-language pipeline separated DENV2-infected and uninfected mosquito video in the authors' controlled dataset.
Limits and context
- An ablation found that fine-tuning and visual representations drove accuracy while language supplied semantic alignment; the study is a dataset result, not a clinical or field diagnostic validation.
Key claims
A vision-language pipeline separated DENV2-infected and uninfected mosquito video in the authors' controlled dataset.
Qualification: An ablation found that fine-tuning and visual representations drove accuracy while language supplied semantic alignment; the study is a dataset result, not a clinical or field diagnostic validation.
Evidence: source-2026-08-15-005
Sources
- arXiv preprint 2608.12677arXiv · primary research
Corrections
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