
DuoSegNet: Adversarial DualViews for SemiSupervised Medical Image Segmentation
Segmentation of images is a longstanding challenge in medical AI. This ...
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SemiSupervised Metric Learning: A Deep Resurrection
Distance Metric Learning (DML) seeks to learn a discriminative embedding...
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Performance Evaluation of Adversarial Attacks: Discrepancies and Solutions
Recently, adversarial attack methods have been developed to challenge th...
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On Learning the Geodesic Path for Incremental Learning
Neural networks notoriously suffer from the problem of catastrophic forg...
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Learning LogDeterminant Divergences for Positive Definite Matrices
Representations in the form of Symmetric Positive Definite (SPD) matrice...
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Learning to Continually Learn Rapidly from Few and Noisy Data
Neural networks suffer from catastrophic forgetting and are unable to se...
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Semanticaware Knowledge Distillation for FewShot ClassIncremental Learning
Fewshot class incremental learning (FSCIL) portrays the problem of lear...
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Improving Medical Image Classification with Label Noise Using Dualuncertainty Estimation
Deep neural networks are known to be datadriven and label noise can hav...
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UncertaintyAware Deep Calibrated Salient Object Detection
Existing deep neural network based salient object detection (SOD) method...
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Set Augmented Triplet Loss for Video Person ReIdentification
Modern video person reidentification (reID) machines are often trained...
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Hierarchical Neural Architecture Search for Deep Stereo Matching
To reduce the human efforts in neural network design, Neural Architectur...
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Channel Recurrent Attention Networks for Video Pedestrian Retrieval
Full attention, which generates an attention value per element of the in...
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Unsupervised Deep Metric Learning via Orthogonality based Probabilistic Loss
Metric learning is an important problem in machine learning. It aims to ...
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MTL2L: A Context Aware Neural Optimiser
Learning to learn (L2L) trains a metalearner to assist the learning of ...
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CrossCorrelated Attention Networks for Person ReIdentification
Deep neural networks need to make robust inference in the presence of oc...
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Bridge the Domain Gap Between Ultrawidefield and Traditional Fundus Images via Adversarial Domain Adaptation
For decades, advances in retinal imaging technology have enabled effecti...
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Affinity guided Geometric SemiSupervised Metric Learning
In this paper, we address the semisupervised metric learning problem, w...
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A Probabilistic approach for Learning Embeddings without Supervision
For challenging machine learning problems such as zeroshot learning and...
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Neural Collaborative Subspace Clustering
We introduce the Neural Collaborative Subspace Clustering, a neural mode...
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Scalable Deep kSubspace Clustering
Subspace clustering algorithms are notorious for their scalability issue...
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Block Mean Approximation for Efficient Second Order Optimization
Advanced optimization algorithms such as Newton method and AdaGrad benef...
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Deep Unsupervised Saliency Detection: A Multiple Noisy Labeling Perspective
The success of current deep saliency detection methods heavily depends o...
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Devon: Deformable Volume Network for Learning Optical Flow
We propose a lightweight neural network model, Deformable Volume Network...
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Museum Exhibit Identification Challenge for Domain Adaptation and Beyond
In this paper, we approach an open problem of artwork identification and...
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Learning Discriminative AlphaBetadivergence for Positive Definite Matrices (Extended Version)
Symmetric positive definite (SPD) matrices are useful for capturing seco...
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Generalized Rank Pooling for Activity Recognition
Most popular deep models for action recognition split video sequences in...
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Learning an Invariant Hilbert Space for Domain Adaptation
This paper introduces a learning scheme to construct a Hilbert space (i....
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Generalized BackPropagation, Étude De Cas: Orthogonality
This paper introduces an extension of the backpropagation algorithm that...
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Analyzing Linear Dynamical Systems: From Modeling to Coding and Learning
Encoding timeseries with Linear Dynamical Systems (LDSs) leads to rich ...
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Dimensionality Reduction on SPD Manifolds: The Emergence of GeometryAware Methods
Representing images and videos with Symmetric Positive Definite (SPD) ma...
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Beyond Gauss: ImageSet Matching on the Riemannian Manifold of PDFs
Stateoftheart imageset matching techniques typically implicitly mode...
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When VLAD met Hilbert
Vectors of Locally Aggregated Descriptors (VLAD) have emerged as powerfu...
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Optimizing Over Radial Kernels on Compact Manifolds
We tackle the problem of optimizing over all possible positive definite ...
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A Framework for Shape Analysis via Hilbert Space Embedding
We propose a framework for 2D shape analysis using positive definite ker...
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Kernel Methods on the Riemannian Manifold of Symmetric Positive Definite Matrices
Symmetric Positive Definite (SPD) matrices have become popular to encode...
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Kernel Methods on Riemannian Manifolds with Gaussian RBF Kernels
In this paper, we develop an approach to exploiting kernel methods with ...
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Kernel Coding: General Formulation and Special Cases
Representing images by compact codes has proven beneficial for many visu...
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Sparse Coding on Symmetric Positive Definite Manifolds using Bregman Divergences
This paper introduces sparse coding and dictionary learning for Symmetri...
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MultiShot Person ReIdentification via Relational Stein Divergence
Person reidentification is particularly challenging due to significant ...
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Extrinsic Methods for Coding and Dictionary Learning on Grassmann Manifolds
Sparsitybased representations have recently led to notable results in v...
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Dictionary Learning and Sparse Coding on Grassmann Manifolds: An Extrinsic Solution
Recent advances in computer vision and machine learning suggest that a w...
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