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View article: Uniform Loss vs. Specialized Optimization: A Comparative Analysis in Multi-Task Learning
Uniform Loss vs. Specialized Optimization: A Comparative Analysis in Multi-Task Learning Open
Specialized Multi-Task Optimizers (SMTOs) balance task learning in Multi-Task Learning by addressing issues like conflicting gradients and differing gradient norms, which hinder equal-weighted task training. However, recent critiques sugge…
View article: Drone-based fault recognition in power systems: a systematic review of intelligent methods
Drone-based fault recognition in power systems: a systematic review of intelligent methods Open
Electrical power systems are susceptible to several damaging effects, potentially leading to faults reaching safety limits and posing critical operational risks. Traditionally, manual inspection has been employed to detect such faults; how…
View article: Global Localization using OpenStreetMap and Elevation Offsets
Global Localization using OpenStreetMap and Elevation Offsets Open
Localization is a critical component in autonomous vehicle navigation stacks. While GNSS-only localization cannot be fully reliable and available all the time, localization based on 3D high-definition (HD) maps have to be robust to world c…
View article: Second-Order Position-Based Visual Servoing of a Robot Manipulator
Second-Order Position-Based Visual Servoing of a Robot Manipulator Open
Visual Servoing is an established approach for controlling robots using visual feedback. Most controllers in this domain generate velocity control signals to guide the cameras to desired positions and orientations. However, the dynamic cha…
View article: DPO: Direct Planar Odometry with Stereo Camera
DPO: Direct Planar Odometry with Stereo Camera Open
Nowadays, state-of-the-art direct visual odometry (VO) methods essentially rely on points to estimate the pose of the camera and reconstruct the environment. Direct Sparse Odometry (DSO) became the standard technique and many approaches ha…
View article: Semantic SuperPoint: A Deep Semantic Descriptor
Semantic SuperPoint: A Deep Semantic Descriptor Open
Several SLAM methods benefit from the use of semantic information. Most integrate photometric methods with high-level semantics such as object detection and semantic segmentation. We propose that adding a semantic segmentation decoder in a…
View article: Leveraging convergence behavior to balance conflicting tasks in multi-task learning
Leveraging convergence behavior to balance conflicting tasks in multi-task learning Open
Multi-Task Learning is a learning paradigm that uses correlated tasks to improve performance generalization. A common way to learn multiple tasks is through the hard parameter sharing approach, in which a single architecture is used to sha…
View article: Table of contents
Table of contents Open
step methodology . . .
View article: Sparse Road Network Model for Autonomous Navigation Using Clothoids
Sparse Road Network Model for Autonomous Navigation Using Clothoids Open
To autonomously navigate in traffic roads, an Autonomous Vehicle must take into account perception information, as well as the topological and geometric structure of the environment it is inserted in. Specifically in urban scenarios, the v…
View article: Sparse-to-Continuous: Enhancing Monocular Depth Estimation using Occupancy Maps
Sparse-to-Continuous: Enhancing Monocular Depth Estimation using Occupancy Maps Open
This paper addresses the problem of single image depth estimation (SIDE),\nfocusing on improving the quality of deep neural network predictions. In a\nsupervised learning scenario, the quality of predictions is intrinsically\nrelated to th…