FOSNet fuses object and scene features via CNN and uses scene coherence loss to report SOTA accuracies of 60.14% on Places2 and 90.37% on MIT Indoor67.
A survey on transfer learning
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.CV 2years
2019 2verdicts
UNVERDICTED 2representative citing papers
Proposes weighted self-incremental transfer learning to address class imbalance in 3D point cloud semantic segmentation and reports a new benchmark on the KITTI dataset.
citing papers explorer
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FOSNet: An End-to-End Trainable Deep Neural Network for Scene Recognition
FOSNet fuses object and scene features via CNN and uses scene coherence loss to report SOTA accuracies of 60.14% on Places2 and 90.37% on MIT Indoor67.
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End-to-End 3D-PointCloud Semantic Segmentation for Autonomous Driving
Proposes weighted self-incremental transfer learning to address class imbalance in 3D point cloud semantic segmentation and reports a new benchmark on the KITTI dataset.