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Found 12 papers, 2 papers with code
Prepared for the Worst: A Learning-Based Adversarial Attack for Resilience Analysis of the ICP Algorithm
This paper presents a novel method to assess the resilience of the Iterative Closest Point (ICP) algorithm via deep-learning-based attacks on lidar point clouds.
SST-ReversibleNet: Reversible-prior-based Spectral-Spatial Transformer for Efficient Hyperspectral Image Reconstruction
The reconstruction subnet in the network then learns the mapping of the residuals to the true values to improve reconstruction accuracy.
Efficient Single-Image Depth Estimation on Mobile Devices, Mobile AI & AIM 2022 Challenge: Report
Various depth estimation models are now widely used on many mobile and IoT devices for image segmentation, bokeh effect rendering, object tracking and many other mobile tasks.
Near-Linear Time and Fixed-Parameter Tractable Algorithms for Tensor Decompositions
Our key technique is a method for obtaining subspace embeddings with a number of rows polynomial in $q$ for a matrix which is the flattening of a tensor train of $q$ tensors.
Using Deep Learning Sequence Models to Identify SARS-CoV-2 Divergence
SARS-CoV-2 is an upper respiratory system RNA virus that has caused over 3 million deaths and infecting over 150 million worldwide as of May 2021.
Fast and Accurate Single-Image Depth Estimation on Mobile Devices, Mobile AI 2021 Challenge: Report
While many solutions have been proposed for this task, they are usually very computationally expensive and thus are not applicable for on-device inference.
Few-Data Guided Learning Upon End-to-End Point Cloud Network for 3D Face Recognition
Without fine-tuning on the test set, the Rank-1 Recognition Rate (RR1) is achieved as follows: 98. 85% on FRGC v2. 0 dataset and 99. 33% on Bosphorus dataset, which proves the effectiveness and the potentiality of our method.
Generative Modeling using the Sliced Wasserstein Distance
While this is particularly true for early GAN formulations, there has been significant empirically motivated and theoretically founded progress to improve stability, for instance, by using the Wasserstein distance rather than the Jenson-Shannon divergence.
Creativity: Generating Diverse Questions using Variational Autoencoders
Generating diverse questions for given images is an important task for computational education, entertainment and AI assistants.
Monocular 3D Object Detection for Autonomous Driving
The focus of this paper is on proposal generation.
Ranked #8 on
Vehicle Pose Estimation
on KITTI Cars Hard
Instance-Level Segmentation for Autonomous Driving with Deep Densely Connected MRFs
Our aim is to provide a pixel-wise instance-level labeling of a monocular image in the context of autonomous driving.
Monocular Object Instance Segmentation and Depth Ordering with CNNs
In this paper we tackle the problem of instance-level segmentation and depth ordering from a single monocular image.
