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Found 32 papers, 11 papers with code
Bidirectional Multi-Step Domain Generalization for Visible-Infrared Person Re-Identification
In particular, our method minimizes the cross-modal gap by identifying and aligning shared prototypes that capture key discriminative features across modalities, then uses multiple bridging steps based on this information to enhance the feature representation.
MoSAR: Monocular Semi-Supervised Model for Avatar Reconstruction using Differentiable Shading
We also introduce a new dataset, named FFHQ-UV-Intrinsics, the first public dataset providing intrinsic face attributes at scale (diffuse, specular, ambient occlusion and translucency maps) for a total of 10k subjects.
Ranked #4 on
3D Face Reconstruction
on REALY
A post-selection algorithm for improving dynamic ensemble selection methods
To evaluate this idea, we introduce the Post-Selection Dynamic Ensemble Selection (PS-DES) approach, a post-selection scheme that evaluates ensembles selected by several DES techniques using different metrics.
Adaptive Generation of Privileged Intermediate Information for Visible-Infrared Person Re-Identification
In this paper, the Adaptive Generation of Privileged Intermediate Information training approach is introduced to adapt and generate a virtual domain that bridges discriminant information between the V and I modalities.
Distance Functions and Normalization Under Stream Scenarios
Data normalization is an essential task when modeling a classification system.
Fusion for Visual-Infrared Person ReID in Real-World Surveillance Using Corrupted Multimodal Data
For realistic evaluation of multimodal (and cross-modal) V-I person ReID models, we propose new challenging corrupted datasets for scenarios where V and I cameras are co-located (CL) and not co-located (NCL).
The choice of scaling technique matters for classification performance
In this paper, we execute a broad experiment comparing the impact of 5 scaling techniques on the performances of 20 classification algorithms among monolithic and ensemble models, applying them to 82 publicly available datasets with varying imbalance ratios.
Multimodal Data Augmentation for Visual-Infrared Person ReID with Corrupted Data
Several deep learning models have been proposed for visible-infrared (V-I) person ReID to recognize individuals from images captured using RGB and IR cameras.
Visible-Infrared Person Re-Identification Using Privileged Intermediate Information
% This paper introduces a novel approach for a creating intermediate virtual domain that acts as bridges between the two main domains (i. e., RGB and IR modalities) during training.
Local overlap reduction procedure for dynamic ensemble selection
Class imbalance is a characteristic known for making learning more challenging for classification models as they may end up biased towards the majority class.
Dynamic Ensemble Selection Using Fuzzy Hyperboxes
Most dynamic ensemble selection (DES) methods utilize the K-Nearest Neighbors (KNN) algorithm to estimate the competence of classifiers in a small region surrounding the query sample.
Knowledge Distillation for Multi-Target Domain Adaptation in Real-Time Person Re-Identification
Despite the recent success of deep learning architectures, person re-identification (ReID) remains a challenging problem in real-word applications.
Selecting and combining complementary feature representations and classifiers for hate speech detection
This work argues that a combination of multiple feature extraction techniques and different classification models is needed.
An Investigation of Feature Selection and Transfer Learning for Writer-Independent Offline Handwritten Signature Verification
We proposed a method based on a global validation strategy with an external archive to control overfitting during the search for the most discriminant representation.
Improving BPSO-based feature selection applied to offline WI handwritten signature verification through overfitting control
This paper investigates the presence of overfitting when using Binary Particle Swarm Optimization (BPSO) to perform the feature selection in a context of Handwritten Signature Verification (HSV).
A white-box analysis on the writer-independent dichotomy transformation applied to offline handwritten signature verification
Among the advantages of this framework is its scalability to deal with some of these challenges and its ease in managing new writers, and hence of being used in a transfer learning context.
Multi-label learning for dynamic model type recommendation
Our proposed framework builds a multi-label meta-classifier responsible for recommending a set of relevant model types based on the local data complexity of the region surrounding each test sample.
ICPRAI 2018 SI: On dynamic ensemble selection and data preprocessing for multi-class imbalance learning
Hence, this paper presents an empirical analysis of dynamic selection techniques and data preprocessing methods for dealing with multi-class imbalanced problems.
META-DES.Oracle: Meta-learning and feature selection for ensemble selection
The key issue in Dynamic Ensemble Selection (DES) is defining a suitable criterion for calculating the classifiers' competence.
On Meta-Learning for Dynamic Ensemble Selection
The meta-features are computed using the training data and used to train a meta-classifier that is able to predict whether or not a base classifier from the pool is competent enough to classify an input instance.
META-DES.H: a dynamic ensemble selection technique using meta-learning and a dynamic weighting approach
In this paper, we propose improvements to the training and generalization phase of the META-DES framework.
Analyzing different prototype selection techniques for dynamic classifier and ensemble selection
The more important step in DES techniques is estimating the competence of the base classifiers for the classification of each specific test sample.
A Method For Dynamic Ensemble Selection Based on a Filter and an Adaptive Distance to Improve the Quality of the Regions of Competence
Dynamic classifier selection systems aim to select a group of classifiers that is most adequate for a specific query pattern.
FIRE-DES++: Enhanced Online Pruning of Base Classifiers for Dynamic Ensemble Selection
Despite being very effective in several classification tasks, Dynamic Ensemble Selection (DES) techniques can select classifiers that classify all samples in the region of competence as being from the same class.
META-DES: A Dynamic Ensemble Selection Framework using Meta-Learning
The meta-features are extracted from the training data and used to train a meta-classifier to predict whether or not a base classifier is competent enough to classify an input instance.
Online local pool generation for dynamic classifier selection: an extended version
Thus, we propose in this work an online pool generation method that produces a locally accurate pool for test samples in difficult regions of the feature space.
Dynamic Ensemble Selection VS K-NN: why and when Dynamic Selection obtains higher classification performance?
Experiments are performed on 18 state-of-the-art DS techniques over 30 classification datasets and results show that DS methods present a significant boost in classification accuracy even though they use the same neighborhood as the K-NN.
An Ensemble Generation Method Based on Instance Hardness
Techniques such as Bagging and Boosting have been successfully applied to a variety of problems.
K-Nearest Oracles Borderline Dynamic Classifier Ensemble Selection
The K-Nearest Oracles Eliminate (KNORA-E) DES selects all classifiers that correctly classify all samples in the region of competence of the test sample, if such classifier exists, otherwise, it removes from the region of competence the sample that is furthest from the test sample, and the process repeats.
On dynamic ensemble selection and data preprocessing for multi-class imbalance learning
Hence, this paper presents an empirical analysis of dynamic selection techniques and data preprocessing methods for dealing with multi-class imbalanced problems.
DESlib: A Dynamic ensemble selection library in Python
DESlib is an open-source python library providing the implementation of several dynamic selection techniques.
A DEEP analysis of the META-DES framework for dynamic selection of ensemble of classifiers
In order to perform a more robust ensemble selection, we proposed the META-DES framework using meta-learning, where multiple criteria are encoded as meta-features and are passed down to a meta-classifier that is trained to estimate the competence level of a given classifier.
