Add to ORKGThis paper introduces the Budding Ensemble Architecture (BEA), a novel reduced ensemble architecture for anchor-based object detection models. Object detection models are crucial in vision-based tasks, particularly in autonomous systems. They should provide precise bounding box detections while also calibrating their predicted confidence scores, leading to higher-quality uncertainty estimates. However, current models may make erroneous decisions due to false positives receiving high scores or true positives being discarded due to low scores. BEA aims to address these issues. The proposed loss functions in BEA improve the confidence score calibration and lower the uncertainty error, which results in a better distinction of true and false pos...
Learning from the limited amount of labeled data to the pre-train model has always been viewed as a ...
Efficient and accurate detection of small objects in manufacturing settings, such as defects and cra...
We introduce a framework for calibrating machine learning models so that their predictions satisfy e...
The quality of training datasets for deep neural networks is a key factor contributing to the accura...
Confluence is a novel non-Intersection over Union (IoU) alternative to Non-Maxima Suppression (NMS) ...
Deep neural networks (DNNs) have made great strides in pushing the state-of-the-art in several chall...
We address the task of open-world class-agnostic object detection, i.e., detecting every object in a...
Calibrated confidence estimates obtained from neural networks are crucial, particularly for safety-c...
The proliferation of Deep Neural Networks has resulted in machine learning systems becoming increasi...
When deploying a model for object detection, a confidence score threshold is chosen to filter out fa...
Accurate uncertainty estimates are essential for deploying deep object detectors in safety-critical ...
We present a list of datasets and their best models with the goal of advancing the state-of-the-art ...
Existing K-nearest neighbor (KNN) retrieval-based methods usually conduct industrial anomaly detecti...
Deep neural networks (DNNs) are known to produce incorrect predictions with very high confidence on ...
When a large feedforward neural network is trained on a small training set, it typically performs po...
Learning from the limited amount of labeled data to the pre-train model has always been viewed as a ...
Efficient and accurate detection of small objects in manufacturing settings, such as defects and cra...
We introduce a framework for calibrating machine learning models so that their predictions satisfy e...
The quality of training datasets for deep neural networks is a key factor contributing to the accura...
Confluence is a novel non-Intersection over Union (IoU) alternative to Non-Maxima Suppression (NMS) ...
Deep neural networks (DNNs) have made great strides in pushing the state-of-the-art in several chall...
We address the task of open-world class-agnostic object detection, i.e., detecting every object in a...
Calibrated confidence estimates obtained from neural networks are crucial, particularly for safety-c...
The proliferation of Deep Neural Networks has resulted in machine learning systems becoming increasi...
When deploying a model for object detection, a confidence score threshold is chosen to filter out fa...
Accurate uncertainty estimates are essential for deploying deep object detectors in safety-critical ...
We present a list of datasets and their best models with the goal of advancing the state-of-the-art ...
Existing K-nearest neighbor (KNN) retrieval-based methods usually conduct industrial anomaly detecti...
Deep neural networks (DNNs) are known to produce incorrect predictions with very high confidence on ...
When a large feedforward neural network is trained on a small training set, it typically performs po...
Learning from the limited amount of labeled data to the pre-train model has always been viewed as a ...
Efficient and accurate detection of small objects in manufacturing settings, such as defects and cra...
We introduce a framework for calibrating machine learning models so that their predictions satisfy e...