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Exploring Plain Vision Transformer Backbones for Object Detection
Yanghao Li,
Hanzi Mao,
Ross Girshick†,
Kaiming He†
arXiv, 2022
paper
A plain, non-hierarchical Vision Transformer (ViT) as a backbone network for object detection.
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A ConvNet for the 2020s
Zhuang Liu,
Hanzi Mao,
Chao-Yuan Wu,
Christoph Feichtenhofer,
Trevor Darrell,
Saining Xie
CVPR, 2022
paper / code
A pure ConvNet model constructed entirely from standard ConvNet modules. ConvNeXt is accurate, efficient, scalable and very simple in design.
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Context-aware Deep Representation Learning for Geo-spatiotemporal Analysis
Hanzi Mao,
Xi Liu,
Nick Duffield,
Hao Yuan,
Shuiwang Ji,
Binayak Mohanty
ICDM, 2020
paper / code
A novel semi-supervised attention-based deep representation model that learns context-aware spatiotemporal representations for prediction tasks.
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Gap Filling of High- Resolution Soil Moisture for SMAP/Sentinel-1: A Two-layer Machine Learning-based Framework
Hanzi Mao,
Dhruva Kathuria,
Nick Duffield,
Binayak Mohanty
Water Resources Research, 2019
paper / code
A new gap‐filled soil moisture product to address the poor spatial and temporal coverage of the SMAP/Sentinel‐1 product.
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