Search Results for author:
Found 15 papers, 9 papers with code
Semantics-aware LiDAR-Only Pseudo Point Cloud Generation for 3D Object Detection
Although LiDAR sensors are crucial for autonomous systems due to providing precise depth information, they struggle with capturing fine object details, especially at a distance, due to sparse and non-uniform data.
FIVA: Facial Image and Video Anonymization and Anonymization Defense
In this paper, we present a new approach for facial anonymization in images and videos, abbreviated as FIVA.
Ranked #1 on
Face Anonymization
on LFW
Depth- and Semantics-aware Multi-modal Domain Translation: Generating 3D Panoramic Color Images from LiDAR Point Clouds
This work presents a new depth- and semantics-aware conditional generative model, named TITAN-Next, for cross-domain image-to-image translation in a multi-modal setup between LiDAR and camera sensors.
FaceDancer: Pose- and Occlusion-Aware High Fidelity Face Swapping
In this work, we present a new single-stage method for subject agnostic face swapping and identity transfer, named FaceDancer.
Ranked #1 on
Face Swapping
on AFLW2000-3D
Semantic State Estimation in Cloth Manipulation Tasks
Understanding of deformable object manipulations such as textiles is a challenge due to the complexity and high dimensionality of the problem.
A Variational Graph Autoencoder for Manipulation Action Recognition and Prediction
The input of the proposed network is a set of semantic graphs which store the spatial relations between subjects and objects in the scene.
MOHAQ: Multi-Objective Hardware-Aware Quantization of Recurrent Neural Networks
On SiLago, we found solutions that achieve 97\% and 86\% of the maximum possible speedup and energy saving, with a minor increase in error.
AI perspectives in Smart Cities and Communities to enable road vehicle automation and smart traffic control
This paper describes AI perspectives in SCC and gives an overview of AI-based technologies used in traffic to enable road vehicle automation and smart traffic control.
SalsaNext: Fast, Uncertainty-aware Semantic Segmentation of LiDAR Point Clouds for Autonomous Driving
In this paper, we introduce SalsaNext for the uncertainty-aware semantic segmentation of a full 3D LiDAR point cloud in real-time.
Ranked #17 on
Robust 3D Semantic Segmentation
on SemanticKITTI-C
Deep Neural Network Compression for Image Classification and Object Detection
Neural networks have been notorious for being computationally expensive.
SalsaNet: Fast Road and Vehicle Segmentation in LiDAR Point Clouds for Autonomous Driving
SalsaNet segments the road, i. e. drivable free-space, and vehicles in the scene by employing the Bird-Eye-View (BEV) image projection of the point cloud.
Introducing the Simulated Flying Shapes and Simulated Planar Manipulator Datasets
We release two artificial datasets, Simulated Flying Shapes and Simulated Planar Manipulator that allow to test the learning ability of video processing systems.
Deep Episodic Memory: Encoding, Recalling, and Predicting Episodic Experiences for Robot Action Execution
We present a novel deep neural network architecture for representing robot experiences in an episodic-like memory which facilitates encoding, recalling, and predicting action experiences.
Semantic Decomposition and Recognition of Long and Complex Manipulation Action Sequences
Understanding continuous human actions is a non-trivial but important problem in computer vision.
Learning the Semantics of Manipulation Action
In this paper we present a formal computational framework for modeling manipulation actions.
