Joost van de Weijer

research

∙ 09/12/2023

Plasticity-Optimized Complementary Networks for Unsupervised Continual Learning

Continuous unsupervised representation learning (CURL) research has grea...

0 Alex Gomez-Villa, et al. ∙

research

∙ 09/06/2023

Continual Evidential Deep Learning for Out-of-Distribution Detection

Uncertainty-based deep learning models have attracted a great deal of in...

0 Eduardo Aguilar, et al. ∙

research

∙ 09/01/2023

Trust your Good Friends: Source-free Domain Adaptation by Reciprocal Neighborhood Clustering

Domain adaptation (DA) aims to alleviate the domain shift between source...

0 Shiqi Yang, et al. ∙

research

∙ 08/31/2023

ScrollNet: Dynamic Weight Importance for Continual Learning

The principle underlying most existing continual learning (CL) methods i...

0 Fei Yang, et al. ∙

research

∙ 08/20/2023

A Comprehensive Empirical Evaluation on Online Continual Learning

Online continual learning aims to get closer to a live learning experien...

0 Albin Soutif--Cormerais, et al. ∙

research

∙ 07/23/2023

Augmented Box Replay: Overcoming Foreground Shift for Incremental Object Detection

In incremental learning, replaying stored samples from previous tasks to...

0 Liu Yuyang, et al. ∙

research

∙ 06/29/2023

Improving Online Continual Learning Performance and Stability with Temporal Ensembles

Neural networks are very effective when trained on large datasets for a ...

0 Albin Soutif--Cormerais, et al. ∙

research

∙ 04/11/2023

Density Map Distillation for Incremental Object Counting

We investigate the problem of incremental learning for object counting, ...

0 Chenshen Wu, et al. ∙

research

∙ 03/28/2023

Projected Latent Distillation for Data-Agnostic Consolidation in Distributed Continual Learning

Distributed learning on the edge often comprises self-centered devices (...

0 Antonio Carta, et al. ∙

research

∙ 03/28/2023

StyleDiffusion: Prompt-Embedding Inversion for Text-Based Editing

A significant research effort is focused on exploiting the amazing capac...

0 Senmao Li, et al. ∙

research

∙ 03/27/2023

3D-Aware Multi-Class Image-to-Image Translation with NeRFs

Recent advances in 3D-aware generative models (3D-aware GANs) combined w...

0 Senmao Li, et al. ∙

research

∙ 03/14/2023

ICICLE: Interpretable Class Incremental Continual Learning

Continual learning enables incremental learning of new tasks without for...

0 Dawid Rymarczyk, et al. ∙

research

∙ 02/01/2023

Towards Label-Efficient Incremental Learning: A Survey

The current dominant paradigm when building a machine learning model is ...

1 Mert Kilickaya, et al. ∙

research

∙ 11/22/2022

Gated Class-Attention with Cascaded Feature Drift Compensation for Exemplar-free Continual Learning of Vision Transformers

In this paper we propose a new method for exemplar-free class incrementa...

0 Marco Cotogni, et al. ∙

research

∙ 10/13/2022

Attribution-aware Weight Transfer: A Warm-Start Initialization for Class-Incremental Semantic Segmentation

In class-incremental semantic segmentation (CISS), deep learning archite...

6 Dipam Goswami, et al. ∙

research

∙ 10/04/2022

Positive Pair Distillation Considered Harmful: Continual Meta Metric Learning for Lifelong Object Re-Identification

Lifelong object re-identification incrementally learns from a stream of ...

0 Kai Wang, et al. ∙

research

∙ 10/03/2022

Attention Distillation: self-supervised vision transformer students need more guidance

Self-supervised learning has been widely applied to train high-quality v...

0 Kai Wang, et al. ∙

research

∙ 06/07/2022

One Ring to Bring Them All: Towards Open-Set Recognition under Domain Shift

In this paper, we investigate open-set recognition with domain shift, wh...

0 Shiqi Yang, et al. ∙

research

∙ 05/30/2022

MVMO: A Multi-Object Dataset for Wide Baseline Multi-View Semantic Segmentation

We present MVMO (Multi-View, Multi-Object dataset): a synthetic dataset ...

0 Aitor Alvarez-Gila, et al. ∙

research

∙ 05/09/2022

Local Prediction Aggregation: A Frustratingly Easy Source-free Domain Adaptation Method

We propose a simple but effective source-free domain adaptation (SFDA) m...

0 Shiqi Yang, et al. ∙

research

∙ 02/16/2022

Planckian jitter: enhancing the color quality of self-supervised visual representations

Several recent works on self-supervised learning are trained by mapping ...

0 Simone Zini, et al. ∙

research

∙ 01/25/2022

Main Product Detection with Graph Networks for Fashion

Computer vision has established a foothold in the online fashion retail ...

0 Vacit Oguz Yazici, et al. ∙

research

∙ 12/30/2021

Continually Learning Self-Supervised Representations with Projected Functional Regularization

Recent self-supervised learning methods are able to learn high-quality i...

0 Alex Gomez-Villa, et al. ∙

research

∙ 12/10/2021

Visual Transformers with Primal Object Queries for Multi-Label Image Classification

Multi-label image classification is about predicting a set of class labe...

0 Vacit Oguz Yazici, et al. ∙

research

∙ 12/04/2021

Hyper-GAN: Transferring Unconditional to Conditional GANs with HyperNetworks

Conditional GANs have matured in recent years and are able to generate h...

0 Héctor Laria, et al. ∙

research

∙ 11/09/2021

Incremental Meta-Learning via Episodic Replay Distillation for Few-Shot Image Recognition

Most meta-learning approaches assume the existence of a very large set o...

0 Kai Wang, et al. ∙

research

∙ 10/21/2021

HCV: Hierarchy-Consistency Verification for Incremental Implicitly-Refined Classification

Human beings learn and accumulate hierarchical knowledge over their life...

6 Kai Wang, et al. ∙

research

∙ 10/09/2021

Class-Balanced Active Learning for Image Classification

Active learning aims to reduce the labeling effort that is required to t...

10 Javad Zolfaghari Bengar, et al. ∙

research

∙ 10/08/2021

Exploiting the Intrinsic Neighborhood Structure for Source-free Domain Adaptation

Domain adaptation (DA) aims to alleviate the domain shift between source...

0 Shiqi Yang, et al. ∙

research

∙ 08/25/2021

Reducing Label Effort: Self-Supervised meets Active Learning

Active learning is a paradigm aimed at reducing the annotation effort by...

0 Javad Zolfaghari Bengar, et al. ∙

research

∙ 08/03/2021

Generalized Source-free Domain Adaptation

Domain adaptation (DA) aims to transfer the knowledge learned from a sou...

0 Shiqi Yang, et al. ∙

research

∙ 07/30/2021

When Deep Learners Change Their Mind: Learning Dynamics for Active Learning

Active learning aims to select samples to be annotated that yield the la...

0 Javad Zolfaghari Bengar, et al. ∙

research

∙ 05/18/2021

ACAE-REMIND for Online Continual Learning with Compressed Feature Replay

Online continual learning aims to learn from a non-IID stream of data fr...

0 Kai Wang, et al. ∙

research

∙ 05/13/2021

TransferI2I: Transfer Learning for Image-to-Image Translation from Small Datasets

Image-to-image (I2I) translation has matured in recent years and is able...

6 Yaxing Wang, et al. ∙

research

∙ 04/28/2021

MineGAN++: Mining Generative Models for Efficient Knowledge Transfer to Limited Data Domains

GANs largely increases the potential impact of generative models. Theref...

29 Yaxing Wang, et al. ∙

research

∙ 04/14/2021

Continual learning in cross-modal retrieval

Multimodal representations and continual learning are two areas closely ...

0 Kai Wang, et al. ∙

research

∙ 03/08/2021

On Implicit Attribute Localization for Generalized Zero-Shot Learning

Zero-shot learning (ZSL) aims to discriminate images from unseen classes...

0 Shiqi Yang, et al. ∙

research

∙ 01/27/2021

Generative Multi-Label Zero-Shot Learning

Multi-label zero-shot learning strives to classify images into multiple ...

10 Akshita Gupta, et al. ∙

research

∙ 12/06/2020

Self-Training for Class-Incremental Semantic Segmentation

We study incremental learning for semantic segmentation where when learn...

0 Lu Yu, et al. ∙

research

∙ 11/11/2020

DeepI2I: Enabling Deep Hierarchical Image-to-Image Translation by Transferring from GANs

Image-to-image translation has recently achieved remarkable results. But...

0 Yaxing Wang, et al. ∙

research

∙ 10/23/2020

Unsupervised Domain Adaptation without Source Data by Casting a BAIT

Unsupervised domain adaptation (UDA) aims to transfer the knowledge lear...

0 Shiqi Yang, et al. ∙

research

∙ 07/31/2020

Learning to Rank for Active Learning: A Listwise Approach

Active learning emerged as an alternative to alleviate the effort to lab...

0 Minghan Li, et al. ∙

research

∙ 07/24/2020

Hallucinating Saliency Maps for Fine-Grained Image Classification for Limited Data Domains

Most of the saliency methods are evaluated on their ability to generate ...

0 Carola Figueroa Flores, et al. ∙

research

∙ 07/13/2020

RATT: Recurrent Attention to Transient Tasks for Continual Image Captioning

Research on continual learning has led to a variety of approaches to mit...

0 Riccardo Del Chiaro, et al. ∙

research

∙ 06/26/2020

Bookworm continual learning: beyond zero-shot learning and continual learning

We propose bookworm continual learning(BCL), a flexible setting where un...

0 Kai Wang, et al. ∙

research

∙ 06/10/2020

Simple and effective localized attribute representations for zero-shot learning

Zero-shot learning (ZSL) aims to discriminate images from unseen classes...

15 Shiqi Yang, et al. ∙

research

∙ 04/22/2020

Distributed Learning and Inference with Compressed Images

Modern computer vision requires processing large amounts of data, both w...

1 Sudeep Katakol, et al. ∙

research

∙ 04/20/2020

Generative Feature Replay For Class-Incremental Learning

Humans are capable of learning new tasks without forgetting previous one...

0 Xialei Liu, et al. ∙

research

∙ 04/01/2020

Semantic Drift Compensation for Class-Incremental Learning

Class-incremental learning of deep networks sequentially increases the n...

0 Lu Yu, et al. ∙

research

∙ 03/30/2020

Semi-supervised Learning for Few-shot Image-to-Image Translation

In the last few years, unpaired image-to-image translation has witnessed...

5 Yaxing Wang, et al. ∙

Joost van de Weijer

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