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04/07/2023
ChiroDiff: Modelling chirographic data with Diffusion Models
Generative modelling over continuous-time geometric constructs, a.k.a su...
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04/04/2023
Learning to Name Classes for Vision and Language Models
Large scale vision and language models can achieve impressive zero-shot ...
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11/16/2022
Region Proposal Network Pre-Training Helps Label-Efficient Object Detection
Self-supervised pre-training, based on the pretext task of instance disc...
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10/17/2022
ZooD: Exploiting Model Zoo for Out-of-Distribution Generalization
Recent advances on large-scale pre-training have shown great potentials ...
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10/04/2022
MEDFAIR: Benchmarking Fairness for Medical Imaging
A multitude of work has shown that machine learning-based medical diagno...
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09/20/2022
Towards 3D VR-Sketch to 3D Shape Retrieval
Growing free online 3D shapes collections dictated research on 3D retrie...
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09/20/2022
Fine-Grained VR Sketching: Dataset and Insights
We present the first fine-grained dataset of 1,497 3D VR sketch and 3D s...
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06/26/2022
Batch-Ensemble Stochastic Neural Networks for Out-of-Distribution Detection
Out-of-distribution (OOD) detection has recently received much attention...
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12/13/2021
Long-tail Recognition via Compositional Knowledge Transfer
In this work, we introduce a novel strategy for long-tail recognition th...
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11/06/2021
Domain Attention Consistency for Multi-Source Domain Adaptation
Most existing multi-source domain adaptation (MSDA) methods minimize the...
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07/05/2021
MixStyle Neural Networks for Domain Generalization and Adaptation
Convolutional neural networks (CNNs) often have poor generalization perf...
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06/19/2021
EvoGrad: Efficient Gradient-Based Meta-Learning and Hyperparameter Optimization
Gradient-based meta-learning and hyperparameter optimization have seen s...
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06/18/2021
Residual Contrastive Learning for Joint Demosaicking and Denoising
The breakthrough of contrastive learning (CL) has fueled the recent succ...
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06/17/2021
Meta-Calibration: Meta-Learning of Model Calibration Using Differentiable Expected Calibration Error
Calibration of neural networks is a topical problem that is becoming inc...
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05/18/2021
Towards Unsupervised Sketch-based Image Retrieval
Current supervised sketch-based image retrieval (SBIR) methods achieve e...
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04/05/2021
Domain Generalization with MixStyle
Though convolutional neural networks (CNNs) have demonstrated remarkable...
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03/29/2021
StyleMeUp: Towards Style-Agnostic Sketch-Based Image Retrieval
Sketch-based image retrieval (SBIR) is a cross-modal matching problem wh...
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03/29/2021
Cloud2Curve: Generation and Vectorization of Parametric Sketches
Analysis of human sketches in deep learning has advanced immensely throu...
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03/25/2021
More Photos are All You Need: Semi-Supervised Learning for Fine-Grained Sketch Based Image Retrieval
A fundamental challenge faced by existing Fine-Grained Sketch-Based Imag...
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03/25/2021
Vectorization and Rasterization: Self-Supervised Learning for Sketch and Handwriting
Self-supervised learning has gained prominence due to its efficacy at le...
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03/22/2021
Context-Aware Layout to Image Generation with Enhanced Object Appearance
A layout to image (L2I) generation model aims to generate a complicated ...
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12/10/2020
Tensor Composition Net for Visual Relationship Prediction
We present a novel Tensor Composition Network (TCN) to predict visual re...
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07/29/2020
Cross-Modal Hierarchical Modelling for Fine-Grained Sketch Based Image Retrieval
Sketch as an image search query is an ideal alternative to text in captu...
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07/07/2020
Learning to Generate Novel Domains for Domain Generalization
This paper focuses on domain generalization (DG), the task of learning f...
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07/04/2020
BézierSketch: A generative model for scalable vector sketches
The study of neural generative models of human sketches is a fascinating...
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06/15/2020
Augmented Sliced Wasserstein Distances
While theoretically appealing, the application of the Wasserstein distan...
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06/15/2020
Flexible Dataset Distillation: Learn Labels Instead of Images
We study the problem of dataset distillation - creating a small set of s...
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04/03/2020
Sequential Learning for Domain Generalization
In this paper we propose a sequential learning framework for Domain Gene...
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03/16/2020
Domain Adaptive Ensemble Learning
The problem of generalizing deep neural networks from multiple source do...
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03/12/2020
Deep Domain-Adversarial Image Generation for Domain Generalisation
Machine learning models typically suffer from the domain shift problem w...
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03/11/2020
Online Meta-Critic Learning for Off-Policy Actor-Critic Methods
Off-Policy Actor-Critic (Off-PAC) methods have proven successful in a va...
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02/24/2020
Sketch Less for More: On-the-Fly Fine-Grained Sketch Based Image Retrieval
Fine-grained sketch-based image retrieval (FG-SBIR) addresses the proble...
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10/17/2019
Deep clustering with concrete k-means
We address the problem of simultaneously learning a k-means clustering a...
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10/15/2019
Learning Generalisable Omni-Scale Representations for Person Re-Identification
An effective person re-identification (re-ID) model should learn feature...
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07/29/2019
Goal-Driven Sequential Data Abstraction
Automatic data abstraction is an important capability for both benchmark...
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05/02/2019
Omni-Scale Feature Learning for Person Re-Identification
As an instance-level recognition problem, person re-identification (ReID...
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04/29/2019
Gated deep neural networks for implied volatility surfaces
In this paper, we propose a gated deep neural network model to predict i...
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01/31/2019
Episodic Training for Domain Generalization
Domain generalization (DG) is the challenging and topical problem of lea...
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01/31/2019
Feature-Critic Networks for Heterogeneous Domain Generalization
The well known domain shift issue causes model performance to degrade wh...
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12/06/2018
Disjoint Label Space Transfer Learning with Common Factorised Space
In this paper, a unified approach is presented to transfer learning that...
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11/17/2018
Deep Comparison: Relation Columns for Few-Shot Learning
Few-shot deep learning is a topical challenge area for scaling visual re...
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06/19/2018
Deep Neural Decision Trees
Deep neural networks have been proven powerful at processing perceptual ...
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04/13/2018
Learning Deep Sketch Abstraction
Human free-hand sketches have been studied in various contexts including...
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11/16/2017
Learning to Compare: Relation Network for Few-Shot Learning
We present a conceptually simple, flexible, and general framework for fe...
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10/09/2017
Deeper, Broader and Artier Domain Generalization
The problem of domain generalization is to learn from multiple training ...
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08/08/2017
Weakly Supervised Image Annotation and Segmentation with Objects and Attributes
We propose to model complex visual scenes using a non-parametric Bayesia...
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06/29/2017
Actor-Critic Sequence Training for Image Captioning
Generating natural language descriptions of images is an important capab...
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03/21/2016
Frankenstein: Learning Deep Face Representations using Small Data
Deep convolutional neural networks have recently proven extremely effect...
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07/28/2015
Zero-Shot Domain Adaptation via Kernel Regression on the Grassmannian
Most visual recognition methods implicitly assume the data distribution ...
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04/09/2015