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Found 4 papers, 1 papers with code
Pyramidal Dense Attention Networks for Lightweight Image Super-Resolution
Recently, deep convolutional neural network methods have achieved an excellent performance in image superresolution (SR), but they can not be easily applied to embedded devices due to large memory cost.
Feedback Pyramid Attention Networks for Single Image Super-Resolution
Recently, convolutional neural network (CNN) based image super-resolution (SR) methods have achieved significant performance improvement.
FusionLane: Multi-Sensor Fusion for Lane Marking Semantic Segmentation Using Deep Neural Networks
This paper proposes a lane marking semantic segmentation method based on LIDAR and camera fusion deep neural network.
Multi-grained Attention Networks for Single Image Super-Resolution
In this paper, we make a thorough investigation on the attention mechanisms in a SR model and shed light on how simple and effective improvements on these ideas improve the state-of-the-arts.
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