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Creators/Authors contains: "Meng, Chenlin"

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  1. ; ; ; ; ; (, IEEE Transactions on Pattern Analysis and Machine Intelligence)
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  2. ; ; ; ; (, International Conference on Machine Learning)
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  3. ; ; ; ; ; ; (, Proceedings of the AAAI Conference on Artificial Intelligence)
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  4. ; ; ; ; ; ; ; ; (, Advances in neural information processing systems)
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  5. ; ; ; (, Advances in neural information processing systems)
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  6. ; ; ; ; ; ; (, IEEE International Conference on Computer Vision workshops)
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  7. ; ; ; (, International Conference on Artificial Intelligence and Statistics)
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  8. ; ; ; ; ; ; ; (, Advances in neural information processing systems)
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  9. ; ; (, Proceedings of the 33rd Annual Conference on Neural Information Processing Systems)
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  10. ; ; ; ; ; ; (, Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence)
    Despite recent progress in computer vision, fine-grained interpretation of satellite images remains challenging because of a lack of labeled training data. To overcome this limitation, we construct a novel dataset called WikiSatNet by pairing geo-referenced Wikipedia articles with satellite imagery of their corresponding locations. We then propose two strategies to learn representations of satellite images by predicting properties of the corresponding articles from the images. Leveraging this new multi-modal dataset, we can drastically reduce the quantity of human-annotated labels and time required for downstream tasks. On the recently released fMoW dataset, our pre-training strategies can boost the performance of a model pre-trained on ImageNet by up to 4.5% in F1 score. 
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