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Found 8 papers, 4 papers with code
Exploiting Object-based and Segmentation-based Semantic Features for Deep Learning-based Indoor Scene Classification
Hence, a novel approach that uses a semantic segmentation mask to provide Hu-moments-based segmentation categories' shape characterization, designated by Segmentation-based Hu-Moments Features (SHMFs), is proposed.
Multispectral Image Segmentation in Agriculture: A Comprehensive Study on Fusion Approaches
Multispectral imagery is frequently incorporated into agricultural tasks, providing valuable support for applications such as image segmentation, crop monitoring, field robotics, and yield estimation.
TReR: A Lightweight Transformer Re-Ranking Approach for 3D LiDAR Place Recognition
Autonomous driving systems often require reliable loop closure detection to guarantee reduced localization drift.
Approaching Test Time Augmentation in the Context of Uncertainty Calibration for Deep Neural Networks
With the rise of Deep Neural Networks, machine learning systems are nowadays ubiquitous in a number of real-world applications, which bears the need for highly reliable models.
A Deep Learning-based Global and Segmentation-based Semantic Feature Fusion Approach for Indoor Scene Classification
This work proposes a novel approach that uses a semantic segmentation mask to obtain a 2D spatial layout of the segmentation-categories across the scene, designated by segmentation-based semantic features (SSFs).
Place recognition survey: An update on deep learning approaches
As part of the localization system, place recognition has benefited from recent developments in other perception tasks such as place categorization or object recognition, namely with the emergence of deep learning (DL) frameworks.
AttDLNet: Attention-based DL Network for 3D LiDAR Place Recognition
LiDAR-based place recognition is one of the key components of SLAM and global localization in autonomous vehicles and robotics applications.
CNN-based Approaches For Cross-Subject Classification in Motor Imagery: From The State-of-The-Art to DynamicNet
In this paper, we first present a review of the most recent studies using deep learning for MI classification, with particular attention to their cross-subject performance.
