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Found 28 papers, 13 papers with code
More Classifiers, Less Forgetting: A Generic Multi-classifier Paradigm for Incremental Learning
Since those regularization strategies are mostly associated with classifier outputs, we propose a MUlti-Classifier (MUC) incremental learning paradigm that integrates an ensemble of auxiliary classifiers to estimate more effective regularization constraints.
MULAN: A Multi Layer Annotated Dataset for Controllable Text-to-Image Generation
To build MuLAn, we developed a training free pipeline which decomposes a monocular RGB image into a stack of RGBA layers comprising of background and isolated instances.
Optimisation-Based Multi-Modal Semantic Image Editing
Image editing affords increased control over the aesthetics and content of generated images.
Learning to Name Classes for Vision and Language Models
Large scale vision and language models can achieve impressive zero-shot recognition performance by mapping class specific text queries to image content.
Content-Diverse Comparisons improve IQA
This restricts the diversity and number of image pairs that the model is exposed to during training.
CLAD: A realistic Continual Learning benchmark for Autonomous Driving
In this paper we describe the design and the ideas motivating a new Continual Learning benchmark for Autonomous Driving (CLAD), that focuses on the problems of object classification and object detection.
Re-examining Distillation For Continual Object Detection
Training models continually to detect and classify objects, from new classes and new domains, remains an open problem.
Long-tail Recognition via Compositional Knowledge Transfer
In this work, we introduce a novel strategy for long-tail recognition that addresses the tail classes' few-shot problem via training-free knowledge transfer.
Learning Compositional Shape Priors for Few-Shot 3D Reconstruction
The impressive performance of deep convolutional neural networks in single-view 3D reconstruction suggests that these models perform non-trivial reasoning about the 3D structure of the output space.
Fewmatch: Dynamic Prototype Refinement for Semi-Supervised Few-Shot Learning
Semi-Supervised Few-shot Learning (SS-FSL) investigates the benefit of incorporating unlabelled data in few-shot settings.
Probabilistic 3D surface reconstruction from sparse MRI information
Surface reconstruction from magnetic resonance (MR) imaging data is indispensable in medical image analysis and clinical research.
Many-shot from Low-shot: Learning to Annotate using Mixed Supervision for Object Detection
Object detection has witnessed significant progress by relying on large, manually annotated datasets.
Low Light Video Enhancement using Synthetic Data Produced with an Intermediate Domain Mapping
Advances in low-light video RAW-to-RGB translation are opening up the possibility of fast low-light imaging on commodity devices (e. g. smartphone cameras) without the need for a tripod.
Image and Video Processing
Few-Shot Single-View 3-D Object Reconstruction with Compositional Priors
In this work we demonstrate experimentally that naive baselines do not apply when the goal is to learn to reconstruct novel objects using very few examples, and that in a \emph{few-shot} learning setting, the network must learn concepts that can be applied to new categories, avoiding rote memorization.
DeepLPF: Deep Local Parametric Filters for Image Enhancement
We introduce a deep neural network, dubbed Deep Local Parametric Filters (DeepLPF), which regresses the parameters of these spatially localized filters that are then automatically applied to enhance the image.
Ranked #8 on
Image Enhancement
on MIT-Adobe 5k
(SSIM on proRGB metric)
Unsupervised Model Personalization while Preserving Privacy and Scalability: An Open Problem
This framework flexibly disentangles user-adaptation into model personalization on the server and local data regularization on the user device, with desirable properties regarding scalability and privacy constraints.
A Multi-Hypothesis Approach to Color Constancy
Firstly, we select a set of candidate scene illuminants in a data-driven fashion and apply them to a target image to generate of set of corrected images.
EHSOD: CAM-Guided End-to-end Hybrid-Supervised Object Detection with Cascade Refinement
In this paper, we study the hybrid-supervised object detection problem, aiming to train a high quality detector with only a limited amount of fullyannotated data and fully exploiting cheap data with imagelevel labels.
A continual learning survey: Defying forgetting in classification tasks
Artificial neural networks thrive in solving the classification problem for a particular rigid task, acquiring knowledge through generalized learning behaviour from a distinct training phase.
Formulating Camera-Adaptive Color Constancy as a Few-shot Meta-Learning Problem
In this work, we propose a new approach that affords fast adaptation to previously unseen cameras, and robustness to changes in capture device by leveraging annotated samples across different cameras and datasets.
Uncertainty Quantification in CNN-Based Surface Prediction Using Shape Priors
Surface reconstruction is a vital tool in a wide range of areas of medical image analysis and clinical research.
Learning Conditioned Graph Structures for Interpretable Visual Question Answering
Our method combines a graph learner module, which learns a question specific graph representation of the input image, with the recent concept of graph convolutions, aiming to learn image representations that capture question specific interactions.
Disease Prediction using Graph Convolutional Networks: Application to Autism Spectrum Disorder and Alzheimer's Disease
Graphs are widely used as a natural framework that captures interactions between individual elements represented as nodes in a graph.
Exploring Heritability of Functional Brain Networks with Inexact Graph Matching
Data-driven brain parcellations aim to provide a more accurate representation of an individual's functional connectivity, since they are able to capture individual variability that arises due to development or disease.
Spectral Graph Convolutions for Population-based Disease Prediction
We demonstrate the potential of the method on the challenging ADNI and ABIDE databases, as a proof of concept of the benefit from integrating contextual information in classification tasks.
Distance Metric Learning using Graph Convolutional Networks: Application to Functional Brain Networks
Evaluating similarity between graphs is of major importance in several computer vision and pattern recognition problems, where graph representations are often used to model objects or interactions between elements.
Comparison of Brain Networks with Unknown Correspondences
In this work we explore a method based on graph edit distance for evaluating graph similarity, when correspondences between network elements are unknown due to different underlying subdivisions of the brain.
Proceedings of the Workshop on Brain Analysis using COnnectivity Networks - BACON 2016
Understanding brain connectivity in a network-theoretic context has shown much promise in recent years.
