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Found 7 papers, 2 papers with code
Robust Data Hiding Using Inverse Gradient Attention
In deep data hiding models, to maximize the encoding capacity, each pixel of the cover image ought to be treated differently since they have different sensitivities w. r. t.
MuMMER: Socially Intelligent Human-Robot Interaction in Public Spaces
In the EU-funded MuMMER project, we have developed a social robot designed to interact naturally and flexibly with users in public spaces such as a shopping mall.
Exploiting temporal consistency for real-time video depth estimation
The temporal consistency loss is combined with the spatial loss to update the model in an end-to-end fashion.
Ranked #4 on
Monocular Depth Estimation
on Mid-Air Dataset
Monocular Depth Estimation with Augmented Ordinal Depth Relationships
In this paper, we propose to improve the performance of metric depth estimation with relative depths collected from stereo movie videos using existing stereo matching algorithm.
Exploiting Depth from Single Monocular Images for Object Detection and Semantic Segmentation
Augmenting RGB data with measured depth has been shown to improve the performance of a range of tasks in computer vision including object detection and semantic segmentation.
Estimating Depth from Monocular Images as Classification Using Deep Fully Convolutional Residual Networks
Then we train fully convolutional deep residual networks to predict the depth label of each pixel.
Temporal Pyramid Pooling Based Convolutional Neural Networks for Action Recognition
One challenge is that video contains a varying number of frames which is incompatible to the standard input format of CNNs.
