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Semantic Scene Understanding with Large Language Models on Unmanned Aerial Vehicles

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Microsoft COCO: Common Objects in Context
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A computer readability formula designed for machine scoring.
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A New Readability Formula for Primary-Grade Reading Materials
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From captions to visual concepts and back
inferredcites →https://doi.org/10.1109/cvpr.2016.91openalex-snapshot
You Only Look Once: Unified, Real-Time Object Detection
inferredcites →https://doi.org/10.1109/cvpr.2017.690openalex-snapshot
YOLO9000: Better, Faster, Stronger
inferredcites →https://doi.org/10.1109/cvpr.2018.00474openalex-snapshot
MobileNetV2: Inverted Residuals and Linear Bottlenecks
inferredcites →https://doi.org/10.1109/cvpr.2018.00636openalex-snapshot
Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
inferredcites →https://doi.org/10.1109/cvpr42600.2020.01079openalex-snapshot
EfficientDet: Scalable and Efficient Object Detection
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Do Massively Pretrained Language Models Make Better Storytellers?
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Hierarchical Neural Story Generation
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Conceptual Captions: A Cleaned, Hypernymed, Image Alt-text Dataset For Automatic Image Captioning
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inferredcites →https://doi.org/10.48550/arxiv.2103.00020openalex-snapshot
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What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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