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Found 7 papers, 1 papers with code
Look Around for Anomalies: Weakly-Supervised Anomaly Detection via Context-Motion Relational Learning
Furthermore, as the relationship between context and motion is important in order to identify the anomalies in complex and diverse scenes, we propose a Context--Motion Interrelation Module (CoMo), which models the relationship between the appearance of the surroundings and motion, rather than utilizing only temporal dependencies or motion information.
MKConv: Multidimensional Feature Representation for Point Cloud Analysis
In this paper, we present Multidimensional Kernel Convolution (MKConv), a novel convolution operator that learns to transform the point feature representation from a vector to a multidimensional matrix.
Ranked #13 on
3D Part Segmentation
on ShapeNet-Part
EdgeConv with Attention Module for Monocular Depth Estimation
Monocular depth estimation is an especially important task in robotics and autonomous driving, where 3D structural information is essential.
Robust Lane Detection via Expanded Self Attention
Modern deep learning methods achieve high performance in lane detection, but it is still difficult to accurately detect lanes in challenging situations such as congested roads and extreme lighting conditions.
Ranked #42 on
Lane Detection
on CULane
False Positive Removal for 3D Vehicle Detection with Penetrated Point Classifier
Recently, researchers have been leveraging LiDAR point cloud for higher accuracy in 3D vehicle detection.
AD-VO: Scale-Resilient Visual Odometry Using Attentive Disparity Map
Visual odometry is an essential key for a localization module in SLAM systems.
Deep Learning-based Limited Feedback Designs for MIMO Systems
We study a deep learning (DL) based limited feedback methods for multi-antenna systems.
