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Found 13 papers, 4 papers with code
Causality from Bottom to Top: A Survey
We study the impact of causality on various fields, its contribution, and its interaction with state-of-the-art approaches.
Reducing the False Positive Rate Using Bayesian Inference in Autonomous Driving Perception
Object recognition is a crucial step in perception systems for autonomous and intelligent vehicles, as evidenced by the numerous research works in the topic.
A Theoretical and Practical Framework for Evaluating Uncertainty Calibration in Object Detection
For this reason, this work presents a novel theoretical and practical framework to evaluate object detection systems in the context of uncertainty calibration.
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.
Survey on Deep Fuzzy Systems in regression applications: a view on interpretability
However, many DL methodologies have complex structures that are not readily transparent to human users.
High-Order Conditional Mutual Information Maximization for dealing with High-Order Dependencies in Feature Selection
The proposed High Order Conditional Mutual Information Maximization (HOCMIM) incorporates high order dependencies into the feature selection procedure and has a straightforward interpretation due to its bottom-up derivation.
Reducing Overconfidence Predictions for Autonomous Driving Perception
In state-of-the-art deep learning for object recognition, SoftMax and Sigmoid functions are most commonly employed as the predictor outputs.
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.
Look and Listen: A Multi-modality Late Fusion Approach to Scene Classification for Autonomous Machines
The image and the audio datasets are first classified independently, using a fine-tuned VGG16 and an evolutionary optimised deep neural network, with accuracies of 89. 27% and 93. 72%, respectively.
LSTM and GPT-2 Synthetic Speech Transfer Learning for Speaker Recognition to Overcome Data Scarcity
In speech recognition problems, data scarcity often poses an issue due to the willingness of humans to provide large amounts of data for learning and classification.
