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Found 6 papers, 2 papers with code
Aligning Multiclass Neural Network Classifier Criterion with Task Performance via $F_β$-Score
Our method extends the $2 \times 2$ binary soft-set confusion matrix to a multiclass $d \times d$ confusion matrix and proposes dynamic adaptation of the threshold value $\tau$, which parameterizes the piecewise-linear Heaviside approximation during run-time.
Towards Inferring Users' Impressions of Robot Performance in Navigation Scenarios
As a more scalable and cost-effective alternative, we study the possibility of predicting people's impressions of robot behavior using non-verbal behavioral cues and machine learning techniques.
Principles and Guidelines for Evaluating Social Robot Navigation Algorithms
A major challenge to deploying robots widely is navigation in human-populated environments, commonly referred to as social robot navigation.
Bridging the Gap: Unifying the Training and Evaluation of Neural Network Binary Classifiers
In this work, we propose a unifying approach to training neural network binary classifiers that combines a differentiable approximation of the Heaviside function with a probabilistic view of the typical confusion matrix values using soft sets.
Improving Social Awareness Through DANTE: A Deep Affinity Network for Clustering Conversational Interactants
We propose a data-driven approach to detect conversational groups by identifying spatial arrangements typical of these focused social encounters.
Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression
By incorporating this generalized $IoU$ ($GIoU$) as a loss into the state-of-the art object detection frameworks, we show a consistent improvement on their performance using both the standard, $IoU$ based, and new, $GIoU$ based, performance measures on popular object detection benchmarks such as PASCAL VOC and MS COCO.
