Uncertainty-Aware Multi-Dimensional Mutual Learning for Brain and Brain Tumor Segmentation.
Sign Up to like & getrecommendations! Published in 2023 at "IEEE journal of biomedical and health informatics"
DOI: 10.1109/jbhi.2023.3274255
Abstract: Existing segmentation methods for brain MRI data usually leverage 3D CNNs on 3D volumes or employ 2D CNNs on 2D image slices. We discovered that while volume-based approaches well respect spatial relationships across slices, slice-based… read more here.
Keywords: uncertainty aware; aware multi; brain; multi dimensional ... See more keywords
Safe Learning for Uncertainty-Aware Planning via Interval MDP Abstraction
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Control Systems Letters"
DOI: 10.1109/lcsys.2022.3173993
Abstract: We study the problem of refining satisfiability bounds for partially-known stochastic systems against planning specifications defined using syntactically co-safe Linear Temporal Logic (scLTL). We propose an abstraction-based approach that iteratively generates high-confidence Interval Markov Decision… read more here.
Keywords: learning uncertainty; planning via; uncertainty aware; aware planning ... See more keywords
Uncertainty-Aware Manipulation Planning Using Gravity and Environment Geometry
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Robotics and Automation Letters"
DOI: 10.1109/lra.2022.3207565
Abstract: Factory automation robot systems often depend on specially-made jigs that precisely position each part, which increases the system's cost and limits flexibility. We propose a method to determine the 3D pose of an object with… read more here.
Keywords: gravity environment; uncertainty aware; geometry;
Uncertainty-Aware Panoptic Segmentation
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Robotics and Automation Letters"
DOI: 10.1109/lra.2023.3256926
Abstract: Reliable scene understanding is indispensable for modern autonomous systems. Current learning-based methods typically try to maximize their performance based on segmentation metrics that only consider the quality of the segmentation. However, for the safe operation… read more here.
Keywords: quality; uncertainty aware; uncertainty; segmentation ... See more keywords
Uncertainty-Aware Variational Inference for Target Tracking
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Transactions on Aerospace and Electronic Systems"
DOI: 10.1109/taes.2022.3184283
Abstract: In the low Earth orbit, target tracking with ground based assets in the context of situational awareness is particularly difficult. Because of the nonlinear state propagation between the moments of measurement arrivals, the inevitably accumulated… read more here.
Keywords: state; lower bound; uncertainty aware; target tracking ... See more keywords
Uncertainty-Aware and Multigranularity Consistent Constrained Model for Semi-Supervised Hashing
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Circuits and Systems for Video Technology"
DOI: 10.1109/tcsvt.2022.3174577
Abstract: Recently, deep semi-supervised hashing methods have attracted increasing attention, which can significantly improve retrieval performance by leveraging abundant unlabeled data. These methods usually generate surrogate supervision signals to learn with unlabeled data, such as neighborhood… read more here.
Keywords: supervised hashing; semi supervised; uncertainty aware; model ... See more keywords
Robust Tracking via Uncertainty-Aware Semantic Consistency
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Transactions on Circuits and Systems for Video Technology"
DOI: 10.1109/tcsvt.2022.3214222
Abstract: Robust tracking has a variety of practical applications. Despite many years of progress, it is still a difficult problem due to enormous uncertainties in real-world scenes. To address this issue, we propose a robust anchor-free… read more here.
Keywords: robust tracking; uncertainty aware; uncertainty; semantic consistency ... See more keywords
Weakly Supervised Region of Interest Extraction Based on Uncertainty-Aware Self-Refinement Learning for Remote Sensing Images
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Geoscience and Remote Sensing"
DOI: 10.1109/tgrs.2022.3199028
Abstract: Region of interest (ROI) extraction plays a significant role in the field of remote sensing image (RSI) processing. Recently, weakly supervised ROI extraction methods have attracted considerable attention due to low labeling cost. Most of… read more here.
Keywords: remote sensing; weakly supervised; uncertainty aware; extraction ... See more keywords
Uncertainty-Aware Gaze Tracking for Assisted Living Environments
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Transactions on Image Processing"
DOI: 10.1109/tip.2023.3253253
Abstract: Effective assisted living environments must be able to infer how their occupants interact in a variety of scenarios. Gaze direction provides strong indications of how a person engages with the environment and its occupants. In… read more here.
Keywords: living environments; assisted living; uncertainty aware; gaze tracking ... See more keywords
Uncertainty-Aware Aggregation for Federated Open Set Domain Adaptation.
Sign Up to like & getrecommendations! Published in 2022 at "IEEE transactions on neural networks and learning systems"
DOI: 10.1109/tnnls.2022.3214930
Abstract: Open set domain adaptation (OSDA) methods have been proposed to leverage the difference between the source and target domains, as well as to recognize the known and unknown classes in the target domain. Such methods… read more here.
Keywords: domain adaptation; set domain; uncertainty aware; uncertainty ... See more keywords
Uncertainty-aware Contrastive Distillation for Incremental Semantic Segmentation
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Pattern Analysis and Machine Intelligence"
DOI: 10.1109/tpami.2022.3163806
Abstract: A fundamental and challenging problem in deep learning is catastrophic forgetting, the tendency of neural networks to fail to preserve the knowledge acquired from old tasks when learning new tasks. This problem has been widely… read more here.
Keywords: contrastive distillation; distillation; uncertainty aware; aware contrastive ... See more keywords