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Found 7 papers, 1 papers with code
Depth-wise Decomposition for Accelerating Separable Convolutions in Efficient Convolutional Neural Networks
Very deep convolutional neural networks (CNNs) have been firmly established as the primary methods for many computer vision tasks.
FusionMapping: Learning Depth Prediction with Monocular Images and 2D Laser Scans
In this paper, we introduce a fusion-based depth prediction method, called FusionMapping.
Cloth Region Segmentation for Robust Grasp Selection
Our approach trains a network to segment the edges and corners of a cloth from a depth image, distinguishing such regions from wrinkles or folds.
Robotics
Robust Instance Tracking via Uncertainty Flow
Current state-of-the-art trackers often fail due to distractorsand large object appearance changes.
Keyframe-Focused Visual Imitation Learning
Imitation learning trains control policies by mimicking pre-recorded expert demonstrations.
Fight fire with fire: countering bad shortcuts in imitation learning with good shortcuts
When operating in partially observed settings, it is important for a control policy to fuse information from a history of observations.
Fighting Fire with Fire: Avoiding DNN Shortcuts through Priming
Across applications spanning supervised classification and sequential control, deep learning has been reported to find "shortcut" solutions that fail catastrophically under minor changes in the data distribution.
