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Pull and concentrate: improving unsupervised semantic segmentation adaptation with cross- and intra-domain consistencies
Unsupervised domain adaptation (UDA) is an important solution for the cross-domain problem in semantic segmentation. Existing segmentation UDA...
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Personalizing Federated Medical Image Segmentation via Local Calibration
Medical image segmentation under federated learning (FL) is a promising direction by allowing multiple clinical sites to collaboratively learn a...
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High-dimensional local polynomial regression with variable selection and dimension reduction
Variable selection and dimension reduction have been considered in nonparametric regression for improving the precision of estimation, via the...
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Multi-start local search algorithm based on a novel objective function for clustering analysis
Clustering is the process of partitioning data into different clusters with the goal of minimizing the difference of objects within each cluster,...
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Super-reparametrizations of weighted CSPs: properties and optimization perspective
The notion of reparametrizations of Weighted CSPs (WCSPs) (also known as equivalence-preserving transformations of WCSPs) is well-known and finds its...
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Virtual Pairwise Consistency in Cost Function Networks
In constraint satisfaction, pairwise consistency (PWC) is a well-known local consistency improving generalized arc consistency in theory but not...
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Neighborhood singleton consistencies
CP solvers predominantly use arc consistency (AC) as the default propagation method for binary constraints. Many stronger consistencies, such as...
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Contrastive Hierarchical Gating Networks for Rating Prediction
Review-based recommendations suffer from text noises and the absence of supervised signals. To address those challenges, we propose a novel...
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Shared Memories Driven by the Intrinsic Memorability of Items
When we experience an event, it feels like our previous experiences, our interpretations of that event (e.g., aesthetics, emotions), and our current...
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Activity propagation in systems of linear inequalities and its relation to block-coordinate descent in linear programs
We study a constraint propagation algorithm to detect infeasibility of a system of linear inequalities over continuous variables, which we call...
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Light field angular super-resolution based on structure and scene information
Light fields contain a wealth of information about real-world scenes and can be easily acquired by commercial light field cameras. However, the...
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3D Correspondence Grou** with Compatibility Features
We present a simple yet effective method for 3D correspondence grou**. The objective is to accurately classify initial correspondences obtained by...
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Just-In-Time Constraint-Based Inference for Qualitative Spatial and Temporal Reasoning
We discuss a research roadmap for going beyond the state of the art in qualitative spatial and temporal reasoning (QSTR). Simply put, QSTR is a major...
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Adaptive constraint propagation in constraint satisfaction: review and evaluation
Several methods for dynamically adapting the local consistency property applied by a CP solver during search have been put forward in recent and...
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Evaluating the Explainability of Neural Rankers
Information retrieval models have witnessed a paradigm shift from unsupervised statistical approaches to feature-based supervised approaches to...
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From Static to Malleable: Improving Flexibility and Compatibility in Burst Buffer File Systems
Numerous burst buffer file systems have been developed in recent years in the context of high-performance computing (HPC). These file systems aim to...
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S \(^2\) Contact: Graph-Based Network for 3D Hand-Object Contact Estimation with Semi-supervised Learning
Despite the recent efforts in accurate 3D annotations in hand and object datasets, there still exist gaps in 3D hand and object reconstructions....
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Representation learning for clustering via building consensus
In this paper, we focus on unsupervised representation learning for clustering of images. Recent advances in deep clustering and unsupervised...
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Learning Representations for Bipartite Graphs Using Multi-task Self-supervised Learning
Representation learning for bipartite graphs is a challenging problem due to its unique structure and characteristics. The primary challenge is the...