… We propose a fusion based on reinforcement learning (RL) which can determine the best …
We propose a priority-based operation fusion with an end-to-end deep reinforcement learning …

We propose an end-to-end learning approach for panoptic segmentation, a novel task unifying
instance (things) and semantic (stuff) segmentation. Our model, TASCNet, uses feature …

We study the problem of multi-focus image fusion, where the key challenge is detecting the
focused regions accurately among multiple partially focused source images. Inspired by the …

… In this study, we used a supervised learning approach, in which … fuse and its bounding
box coordinates pixelwise in the image: xmin, ymin, xmax, and ymax. In a typical deep learning …

… pose estimation rely on Deep Learning. They typically involve … fusing the information along
the way. At the heart of our framework is a trainable fusion scheme that learns how to fuse the …

… and cannot fully benefit from offline learning on large-scale data. We propose a learnable
module, called the asymmetric convolution (ACM), which learns to better capture the semantic …

… We present a system for fusing sentences which are drawn … world examples of sentences
fused by professional journalists … into a joint optimization problem, and learn parameters for this …

… In this work we present a novel approach to sensor fusion using a deep learning method
to learn the relation between camera poses and inertial sensor measurements. A long short-…

… In this paper, we pay particular attention to fuse disparate … We address the challenge of fusing
disparate sentences by … We use the Adam optimizer with a learning rate of 2e-5 with warm-…

Semi-Global Matching (SGM) uses an aggregation scheme to combine costs from multiple
1D scanline optimizations that tends to hurt its accuracy in difficult scenarios. We propose …