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Found 44 papers, 12 papers with code
Convolutional autoencoder-based multimodal one-class classification
We show that such regularizers improve performance.
On Feature Diversity in Energy-based Models
Energy-based learning is a powerful learning paradigm that encapsulates various discriminative and generative approaches.
WLD-Reg: A Data-dependent Within-layer Diversity Regularizer
At each optimization step, neurons at a given layer receive feedback from neurons belonging to higher layers of the hierarchy.
1st Workshop on Maritime Computer Vision (MaCVi) 2023: Challenge Results
The 1$^{\text{st}}$ Workshop on Maritime Computer Vision (MaCVi) 2023 focused on maritime computer vision for Unmanned Aerial Vehicles (UAV) and Unmanned Surface Vehicle (USV), and organized several subchallenges in this domain: (i) UAV-based Maritime Object Detection, (ii) UAV-based Maritime Object Tracking, (iii) USV-based Maritime Obstacle Segmentation and (iv) USV-based Maritime Obstacle Detection.
Computer Vision on X-ray Data in Industrial Production and Security Applications: A Comprehensive Survey
X-ray imaging technology has been used for decades in clinical tasks to reveal the internal condition of different organs, and in recent years, it has become more common in other areas such as industry, security, and geography.
Habitat classification from satellite observations with sparse annotations
Therefore, full segmentation maps are expensive to produce, and training data is often sparse, point-like, and limited to areas accessible by foot.
Efficient CNN with uncorrelated Bag of Features pooling
In this paper, we propose an approach that builds on top of BoF pooling to boost its efficiency by ensuring that the items of the learned dictionary are non-redundant.
Generalized Reference Kernel for One-class Classification
Focusing on small-scale one-class classification, our analysis and experimental results show that the new formulation provides approaches to regularize, adjust the rank, and incorporate additional information into the kernel itself, leading to improved one-class classification accuracy.
Automatic Image Content Extraction: Operationalizing Machine Learning in Humanistic Photographic Studies of Large Visual Archives
Applying machine learning tools to digitized image archives has a potential to revolutionize quantitative research of visual studies in humanities and social sciences.
Non-Linear Spectral Dimensionality Reduction Under Uncertainty
In this paper, we consider the problem of non-linear dimensionality reduction under uncertainty, both from a theoretical and algorithmic perspectives.
Reducing Redundancy in the Bottleneck Representation of the Autoencoders
We tested our approach across different tasks: dimensionality reduction using three different dataset, image compression using the MNIST dataset, and image denoising using fashion MNIST.
Robust channel-wise illumination estimation
We test this approach on the proposed method and show that it can indeed be used to avoid several extreme error cases and, thus, improves the practicality of the proposed technique.
Learning to ignore: rethinking attention in CNNs
Based on this idea, we propose to reformulate the attention mechanism in CNNs to learn to ignore instead of learning to attend.
Improving Neural Network Generalization via Promoting Within-Layer Diversity
Neural networks are composed of multiple layers arranged in a hierarchical structure jointly trained with a gradient-based optimization, where the errors are back-propagated from the last layer back to the first one.
Automatic Main Character Recognition for Photographic Studies
Identifying the main character in images plays an important role in traditional photographic studies and media analysis, but the task is performed manually and can be slow and laborious.
Learning distinct features helps, provably
We study the diversity of the features learned by a two-layer neural network trained with the least squares loss.
Graph-Embedded Subspace Support Vector Data Description
In this paper, we propose a novel subspace learning framework for one-class classification.
Automatic Social Distance Estimation From Images: Performance Evaluation, Test Benchmark, and Algorithm
The World Health Organization (WHO) has provided guidelines on how to reduce the spread of the virus and one of the most important measures is social distancing.
Ensembling object detectors for image and video data analysis
In this paper, we propose a method for ensembling the outputs of multiple object detectors for improving detection performance and precision of bounding boxes on image data.
ON NEURAL NETWORK GENERALIZATION VIA PROMOTING WITHIN-LAYER ACTIVATION DIVERSITY
During the last decade, neural networks have been intensively used to tackle various problems and they have often led to state-of-the-art results.
Speech Command Recognition in Computationally Constrained Environments with a Quadratic Self-organized Operational Layer
Automatic classification of speech commands has revolutionized human computer interactions in robotic applications.
Graph Embedding with Data Uncertainty
spectral-based subspace learning is a common data preprocessing step in many machine learning pipelines.
Collaborative Multi-Robot Systems for Search and Rescue: Coordination and Perception
Autonomous or teleoperated robots have been playing increasingly important roles in civil applications in recent years.
Monte Carlo Dropout Ensembles for Robust Illumination Estimation
Computational color constancy is a preprocessing step used in many camera systems.
AutoSOS: Towards Multi-UAV Systems Supporting Maritime Search and Rescue with Lightweight AI and Edge Computing
Rescue vessels are the main actors in maritime safety and rescue operations.
Probabilistic Color Constancy
In this paper, we propose a novel unsupervised color constancy method, called Probabilistic Color Constancy (PCC).
Saliency-based Weighted Multi-label Linear Discriminant Analysis
The proposed method is based on a probabilistic model for defining the weights of individual samples in a weighted multi-label LDA approach.
Not all domains are equally complex: Adaptive Multi-Domain Learning
The most common approach in multi-domain learning is to form a domain agnostic model, the parameters of which are shared among all domains, and learn a small number of extra domain-specific parameters for each individual new domain.
Ellipsoidal Subspace Support Vector Data Description
In this paper, we propose a novel method for transforming data into a low-dimensional space optimized for one-class classification.
Boosting rare benthic macroinvertebrates taxa identification with one-class classification
One-class classification models are traditionally trained with much fewer samples and they can provide a mechanism to indicate samples potentially belonging to the rare classes for human inspection.
Incremental Fast Subclass Discriminant Analysis
This paper proposes an incremental solution to Fast Subclass Discriminant Analysis (fastSDA).
Automatic image-based identification and biomass estimation of invertebrates
We use this database to test the classification accuracy i. e. how well the species identity of a specimen can be predicted from images taken by the machine.
INTEL-TAU: A Color Constancy Dataset
In this paper, we describe a new large dataset for illumination estimation.
Few-Shot Camera-Adaptive Color Constancy
Image Declipping
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Neural Architecture Search by Estimation of Network Structure Distributions
Our matrix of probabilities is equivalent to the population of models, but allows for discovery of structural irregularities, while being simple to interpret and analyze.
Null Space Analysis for Class-Specific Discriminant Learning
In this paper, we carry out null space analysis for Class-Specific Discriminant Analysis (CSDA) and formulate a number of solutions based on the analysis.
Reversible Privacy Preservation using Multi-level Encryption and Compressive Sensing
Security monitoring via ubiquitous cameras and their more extended in intelligent buildings stand to gain from advances in signal processing and machine learning.
Bag of Color Features For Color Constancy
To further improve the illumination estimation accuracy, we propose a novel attention mechanism for the BoCF model with two variants based on self-attention.
Color Constancy Convolutional Autoencoder
In this paper, we study the importance of pre-training for the generalization capability in the color constancy problem.
Speed-up and multi-view extensions to Subclass Discriminant Analysis
We show that by exploiting the structure of the between-class Laplacian matrix, the eigendecomposition step can be substituted with a much faster process.
Machine Learning Based Analysis of Finnish World War II Photographers
In this paper, we demonstrate the benefits of using state-of-the-art machine learning methods in the analysis of historical photo archives.
Multimodal Subspace Support Vector Data Description
In this paper, we propose a novel method for projecting data from multiple modalities to a new subspace optimized for one-class classification.
Subspace Support Vector Data Description
The method iteratively optimizes the data mapping along with data description in order to define a compact class representation in a low-dimensional feature space.
Human experts vs. machines in taxa recognition
Contrary to previous findings in the literature, we find that for machines following a typical flat classification approach commonly used in machine learning performs better than forcing machines to adopt a hierarchical, local per parent node approach used by human taxonomic experts ($\overline{CE}=13. 8\%$).
Limited Random Walk Algorithm for Big Graph Data Clustering
Graph clustering is an important technique to understand the relationships between the vertices in a big graph.
Social and Information Networks Physics and Society
