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Found 11 papers, 1 papers with code
hear-your-action: human action recognition by ultrasound active sensing
As action recognition from ultrasound active sensing in a non-invasive manner is not well investigated, we create a new dataset for action recognition and conduct a comparison of features for classification.
PALF: Pre-Annotation and Camera-LiDAR Late Fusion for the Easy Annotation of Point Clouds
Unlike 2D image labels, annotating point cloud data is difficult due to the limitations of sparsity, irregularity, and low resolution, which requires more manual work, and the annotation efficiency is much lower than 2D image. Therefore, we propose an annotation algorithm for point cloud data, which is pre-annotation and camera-LiDAR late fusion algorithm to easily and accurately annotate.
Deep Single Image Camera Calibration by Heatmap Regression to Recover Fisheye Images Under ManhattanWorld AssumptionWithout Ambiguity
In orthogonal world coordinates, a Manhattan world lying along cuboid buildings is widely useful for various computer vision tasks.
Masking and Mixing Adversarial Training
Adversarial training is a popular and straightforward technique to defend against the threat of adversarial examples.
Data Augmentation by Selecting Mixed Classes Considering Distance Between Classes
Data augmentation is an essential technique for improving recognition accuracy in object recognition using deep learning.
Few-shot Adaptive Object Detection with Cross-Domain CutMix
In object detection, data amount and cost are a trade-off, and collecting a large amount of data in a specific domain is labor intensive.
Invisible-to-Visible: Privacy-Aware Human Segmentation using Airborne Ultrasound via Collaborative Learning Probabilistic U-Net
They are highly and widely used in tasks such as segmentation.
Invisible-to-Visible: Privacy-Aware Human Instance Segmentation using Airborne Ultrasound via Collaborative Learning Variational Autoencoder
In inference, it is possible to obtain instance segmentation results only from sound images.
Rethinking Generic Camera Models for Deep Single Image Camera Calibration to Recover Rotation and Fisheye Distortion
To address this problem, we propose a generic camera model that has the potential to address various types of distortion.
CutDepth:Edge-aware Data Augmentation in Depth Estimation
It is difficult to collect data on a large scale in a monocular depth estimation because the task requires the simultaneous acquisition of RGB images and depths.
Ranked #44 on
Monocular Depth Estimation
on NYU-Depth V2
(using extra training data)
Explain to Fix: A Framework to Interpret and Correct DNN Object Detector Predictions
We qualitatively and quantitatively show that the proposed explanation method can be used to find image features which cause failures in DNN object detection.
