  In this paper, we propose an adaptive weighting regression (AWR) method to
leverage the advantages of both detection-based and regression-based methods.
Hand joint coordinates are estimated as discrete integration of all pixels in
dense representation, guided by adaptive weight maps. This learnable
aggregation process introduces both dense and joint supervision that allows
end-to-end training and brings adaptability to weight maps, making the network
more accurate and robust. Comprehensive exploration experiments are conducted
to validate the effectiveness and generality of AWR under various experimental
settings, especially its usefulness for different types of dense representation
and input modality. Our method outperforms other state-of-the-art methods on
four publicly available datasets, including NYU, ICVL, MSRA and HANDS 2017
dataset.
