Search Results for author: Joost Van de Weijer

Found 116 papers, 69 papers with code

Calibrating Higher-Order Statistics for Few-Shot Class-Incremental Learning with Pre-trained Vision Transformers

1 code implementation9 Apr 2024 Dipam Goswami, Bartłomiej Twardowski, Joost Van de Weijer

FSCIL methods start with a many-shot first task to learn a very good feature extractor and then move to the few-shot setting from the second task onwards.

Few-Shot Class-Incremental Learning Incremental Learning +2

Accelerated Inference and Reduced Forgetting: The Dual Benefits of Early-Exit Networks in Continual Learning

no code implementations12 Mar 2024 Filip Szatkowski, Fei Yang, Bartłomiej Twardowski, Tomasz Trzciński, Joost Van de Weijer

We assess the accuracy and computational cost of various continual learning techniques enhanced with early-exits and TLC across standard class-incremental learning benchmarks such as 10 split CIFAR100 and ImageNetSubset and show that TLC can achieve the accuracy of the standard methods using less than 70\% of their computations.

Class Incremental Learning Incremental Learning

Get What You Want, Not What You Don't: Image Content Suppression for Text-to-Image Diffusion Models

1 code implementation8 Feb 2024 Senmao Li, Joost Van de Weijer, Taihang Hu, Fahad Shahbaz Khan, Qibin Hou, Yaxing Wang, Jian Yang

However, these models struggle to effectively suppress the generation of undesired content, which is explicitly requested to be omitted from the generated image in the prompt.

Elastic Feature Consolidation for Cold Start Exemplar-free Incremental Learning

1 code implementation6 Feb 2024 Simone Magistri, Tomaso Trinci, Albin Soutif-Cormerais, Joost Van de Weijer, Andrew D. Bagdanov

Experimental results on CIFAR-100, Tiny-ImageNet, ImageNet-Subset and ImageNet-1K demonstrate that Elastic Feature Consolidation is better able to learn new tasks by maintaining model plasticity and significantly outperform the state-of-the-art.

Class Incremental Learning Incremental Learning

Enhancing Perceptual Quality in Video Super-Resolution through Temporally-Consistent Detail Synthesis using Diffusion Models

1 code implementation27 Nov 2023 Claudio Rota, Marco Buzzelli, Joost Van de Weijer

We demonstrate the effectiveness of StableVSR in enhancing the perceptual quality of upscaled videos compared to existing state-of-the-art methods for VSR.

Image Super-Resolution Video Super-Resolution

IterInv: Iterative Inversion for Pixel-Level T2I Models

1 code implementation30 Oct 2023 Chuanming Tang, Kai Wang, Joost Van de Weijer

Current image editing techniques are relying on DDIM inversion as a common practice based on the Latent Diffusion Models (LDM).

Super-Resolution

Exploiting Image-Related Inductive Biases in Single-Branch Visual Tracking

no code implementations30 Oct 2023 Chuanming Tang, Kai Wang, Joost Van de Weijer, Jianlin Zhang, YongMei Huang

Moreover, the effectiveness of discriminative trackers remains constrained due to the adoption of the dual-branch pipeline.

Visual Tracking

Technical Report for ICCV 2023 Visual Continual Learning Challenge: Continuous Test-time Adaptation for Semantic Segmentation

no code implementations20 Oct 2023 Damian Sójka, Yuyang Liu, Dipam Goswami, Sebastian Cygert, Bartłomiej Twardowski, Joost Van de Weijer

Each sequence is composed of 401 images and starts with the source domain, then gradually drifts to a different one (changing weather or time of day) until the middle of the sequence.

Continual Learning Semantic Segmentation +1

Dynamic Prompt Learning: Addressing Cross-Attention Leakage for Text-Based Image Editing

1 code implementation NeurIPS 2023 Kai Wang, Fei Yang, Shiqi Yang, Muhammad Atif Butt, Joost Van de Weijer

Large-scale text-to-image generative models have been a ground-breaking development in generative AI, with diffusion models showing their astounding ability to synthesize convincing images following an input text prompt.

Text-based Image Editing

Plasticity-Optimized Complementary Networks for Unsupervised Continual Learning

1 code implementation12 Sep 2023 Alex Gomez-Villa, Bartlomiej Twardowski, Kai Wang, Joost Van de Weijer

In the second phase, we combine this new knowledge with the previous network in an adaptation-retrospection phase to avoid forgetting and initialize a new expert with the knowledge of the old network.

Representation Learning Self-Supervised Learning +1

Continual Evidential Deep Learning for Out-of-Distribution Detection

1 code implementation6 Sep 2023 Eduardo Aguilar, Bogdan Raducanu, Petia Radeva, Joost Van de Weijer

Uncertainty-based deep learning models have attracted a great deal of interest for their ability to provide accurate and reliable predictions.

Continual Learning Out-of-Distribution Detection

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

no code implementations1 Sep 2023 Shiqi Yang, Yaxing Wang, Joost Van de Weijer, Luis Herranz, Shangling Jui, Jian Yang

We capture this intrinsic structure by defining local affinity of the target data, and encourage label consistency among data with high local affinity.

Clustering Source-Free Domain Adaptation

ScrollNet: Dynamic Weight Importance for Continual Learning

1 code implementation31 Aug 2023 Fei Yang, Kai Wang, Joost Van de Weijer

The importance of weights for each task can be determined either explicitly through learning a task-specific mask during training (e. g., parameter isolation-based approaches) or implicitly by introducing a regularization term (e. g., regularization-based approaches).

Continual Learning

A Comprehensive Empirical Evaluation on Online Continual Learning

2 code implementations20 Aug 2023 Albin Soutif--Cormerais, Antonio Carta, Andrea Cossu, Julio Hurtado, Hamed Hemati, Vincenzo Lomonaco, Joost Van de Weijer

Online continual learning aims to get closer to a live learning experience by learning directly on a stream of data with temporally shifting distribution and by storing a minimum amount of data from that stream.

Class Incremental Learning Image Classification

Augmented Box Replay: Overcoming Foreground Shift for Incremental Object Detection

1 code implementation ICCV 2023 Liu Yuyang, Cong Yang, Goswami Dipam, Liu Xialei, Joost Van de Weijer

Foreground shift only occurs when replaying images of previous tasks and refers to the fact that their background might contain foreground objects of the current task.

Incremental Learning object-detection +1

Density Map Distillation for Incremental Object Counting

no code implementations11 Apr 2023 Chenshen Wu, Joost Van de Weijer

We investigate the problem of incremental learning for object counting, where a method must learn to count a variety of object classes from a sequence of datasets.

Incremental Learning Object +1

StyleDiffusion: Prompt-Embedding Inversion for Text-Based Editing

1 code implementation28 Mar 2023 Senmao Li, Joost Van de Weijer, Taihang Hu, Fahad Shahbaz Khan, Qibin Hou, Yaxing Wang, Jian Yang

A significant research effort is focused on exploiting the amazing capacities of pretrained diffusion models for the editing of images.

Text-based Image Editing

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

1 code implementation28 Mar 2023 Antonio Carta, Andrea Cossu, Vincenzo Lomonaco, Davide Bacciu, Joost Van de Weijer

We formalize this problem as a Distributed Continual Learning scenario, where SCD adapt to local tasks and a CL model consolidates the knowledge from the resulting stream of models without looking at the SCD's private data.

Continual Learning Knowledge Distillation

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

1 code implementation CVPR 2023 Senmao Li, Joost Van de Weijer, Yaxing Wang, Fahad Shahbaz Khan, Meiqin Liu, Jian Yang

In the second step, based on the well-trained multi-class 3D-aware GAN architecture, that preserves view-consistency, we construct a 3D-aware I2I translation system.

Image-to-Image Translation Translation

ICICLE: Interpretable Class Incremental Continual Learning

1 code implementation ICCV 2023 Dawid Rymarczyk, Joost Van de Weijer, Bartosz Zieliński, Bartłomiej Twardowski

Continual learning enables incremental learning of new tasks without forgetting those previously learned, resulting in positive knowledge transfer that can enhance performance on both new and old tasks.

Class Incremental Learning Incremental Learning +1

Towards Label-Efficient Incremental Learning: A Survey

1 code implementation1 Feb 2023 Mert Kilickaya, Joost Van de Weijer, Yuki M. Asano

The current dominant paradigm when building a machine learning model is to iterate over a dataset over and over until convergence.

Incremental Learning Self-Supervised Learning

Endpoints Weight Fusion for Class Incremental Semantic Segmentation

no code implementations CVPR 2023 Jia-Wen Xiao, Chang-Bin Zhang, Jiekang Feng, Xialei Liu, Joost Van de Weijer, Ming-Ming Cheng

In our method, the model containing old knowledge is fused with the model retaining new knowledge in a dynamic fusion manner, strengthening the memory of old classes in ever-changing distributions.

Class-Incremental Semantic Segmentation Incremental Learning +1

Exemplar-free Continual Learning of Vision Transformers via Gated Class-Attention and Cascaded Feature Drift Compensation

1 code implementation22 Nov 2022 Marco Cotogni, Fei Yang, Claudio Cusano, Andrew D. Bagdanov, Joost Van de Weijer

Secondly, we propose a new method of feature drift compensation that accommodates feature drift in the backbone when learning new tasks.

Continual Learning

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

1 code implementation13 Oct 2022 Dipam Goswami, René Schuster, Joost Van de Weijer, Didier Stricker

In class-incremental semantic segmentation (CISS), deep learning architectures suffer from the critical problems of catastrophic forgetting and semantic background shift.

Overlapped 100-10 Overlapped 100-5 +7

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

1 code implementation3 Oct 2022 Kai Wang, Fei Yang, Joost Van de Weijer

In experiments on ImageNet-Subset and ImageNet-1K, we show that our method AttnDistill outperforms existing self-supervised knowledge distillation (SSKD) methods and achieves state-of-the-art k-NN accuracy compared with self-supervised learning (SSL) methods learning from scratch (with the ViT-S model).

Knowledge Distillation Self-Supervised Learning

OneRing: A Simple Method for Source-free Open-partial Domain Adaptation

1 code implementation7 Jun 2022 Shiqi Yang, Yaxing Wang, Kai Wang, Shangling Jui, Joost Van de Weijer

In this paper, we investigate Source-free Open-partial Domain Adaptation (SF-OPDA), which addresses the situation where there exist both domain and category shifts between source and target domains.

Domain Generalization Open Set Learning +2

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

no code implementations30 May 2022 Aitor Alvarez-Gila, Joost Van de Weijer, Yaxing Wang, Estibaliz Garrote

We present MVMO (Multi-View, Multi-Object dataset): a synthetic dataset of 116, 000 scenes containing randomly placed objects of 10 distinct classes and captured from 25 camera locations in the upper hemisphere.

Object Segmentation +1

Attracting and Dispersing: A Simple Approach for Source-free Domain Adaptation

1 code implementation9 May 2022 Shiqi Yang, Yaxing Wang, Kai Wang, Shangling Jui, Joost Van de Weijer

Treating SFDA as an unsupervised clustering problem and following the intuition that local neighbors in feature space should have more similar predictions than other features, we propose to optimize an objective of prediction consistency.

Clustering Source-Free Domain Adaptation

Towards Exemplar-Free Continual Learning in Vision Transformers: an Account of Attention, Functional and Weight Regularization

1 code implementation24 Mar 2022 Francesco Pelosin, Saurav Jha, Andrea Torsello, Bogdan Raducanu, Joost Van de Weijer

In this paper, we investigate the continual learning of Vision Transformers (ViT) for the challenging exemplar-free scenario, with special focus on how to efficiently distill the knowledge of its crucial self-attention mechanism (SAM).

Continual Learning

Main Product Detection with Graph Networks for Fashion

no code implementations25 Jan 2022 Vacit Oguz Yazici, LongLong Yu, Arnau Ramisa, Luis Herranz, Joost Van de Weijer

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

Continually Learning Self-Supervised Representations with Projected Functional Regularization

1 code implementation30 Dec 2021 Alex Gomez-Villa, Bartlomiej Twardowski, Lu Yu, Andrew D. Bagdanov, Joost Van de Weijer

Recent self-supervised learning methods are able to learn high-quality image representations and are closing the gap with supervised approaches.

Continual Learning Incremental Learning +1

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

1 code implementation10 Dec 2021 Vacit Oguz Yazici, Joost Van de Weijer, LongLong Yu

However, inputting the same set of object queries to different decoder layers hinders the training: it results in lower performance and delays convergence.

Multi-Label Classification Multi-Label Image Classification +3

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

1 code implementation9 Nov 2021 Kai Wang, Xialei Liu, Andy Bagdanov, Luis Herranz, Shangling Jui, Joost Van de Weijer

We propose an approach to IML, which we call Episodic Replay Distillation (ERD), that mixes classes from the current task with class exemplars from previous tasks when sampling episodes for meta-learning.

Continual Learning Knowledge Distillation +1

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

1 code implementation21 Oct 2021 Kai Wang, Xialei Liu, Luis Herranz, Joost Van de Weijer

To overcome forgetting in this benchmark, we propose Hierarchy-Consistency Verification (HCV) as an enhancement to existing continual learning methods.

Classification Continual Learning +1

Class-Balanced Active Learning for Image Classification

1 code implementation9 Oct 2021 Javad Zolfaghari Bengar, Joost Van de Weijer, Laura Lopez Fuentes, Bogdan Raducanu

Results on three datasets showed that the method is general (it can be combined with most existing active learning algorithms) and can be effectively applied to boost the performance of both informative and representative-based active learning methods.

Active Learning Classification +1

Exploiting the Intrinsic Neighborhood Structure for Source-free Domain Adaptation

2 code implementations NeurIPS 2021 Shiqi Yang, Yaxing Wang, Joost Van de Weijer, Luis Herranz, Shangling Jui

In this paper, we address the challenging source-free domain adaptation (SFDA) problem, where the source pretrained model is adapted to the target domain in the absence of source data.

Source-Free Domain Adaptation

Distilling GANs with Style-Mixed Triplets for X2I Translation with Limited Data

no code implementations ICLR 2022 Yaxing Wang, Joost Van de Weijer, Lu Yu, Shangling Jui

Therefore, we investigate knowledge distillation to transfer knowledge from a high-quality unconditioned generative model (e. g., StyleGAN) to a conditioned synthetic image generation modules in a variety of systems.

Image Generation Knowledge Distillation +2

Reducing Label Effort: Self-Supervised meets Active Learning

no code implementations25 Aug 2021 Javad Zolfaghari Bengar, Joost Van de Weijer, Bartlomiej Twardowski, Bogdan Raducanu

Our experiments reveal that self-training is remarkably more efficient than active learning at reducing the labeling effort, that for a low labeling budget, active learning offers no benefit to self-training, and finally that the combination of active learning and self-training is fruitful when the labeling budget is high.

Active Learning Object Recognition

Generalized Source-free Domain Adaptation

1 code implementation ICCV 2021 Shiqi Yang, Yaxing Wang, Joost Van de Weijer, Luis Herranz, Shangling Jui

In this paper, we propose a new domain adaptation paradigm called Generalized Source-free Domain Adaptation (G-SFDA), where the learned model needs to perform well on both the target and source domains, with only access to current unlabeled target data during adaptation.

Source-Free Domain Adaptation

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

no code implementations30 Jul 2021 Javad Zolfaghari Bengar, Bogdan Raducanu, Joost Van de Weijer

Many methods approach this problem by measuring the informativeness of samples and do this based on the certainty of the network predictions for samples.

Active Learning Informativeness

On the importance of cross-task features for class-incremental learning

1 code implementation22 Jun 2021 Albin Soutif--Cormerais, Marc Masana, Joost Van de Weijer, Bartłomiej Twardowski

We also define a new forgetting measure for class-incremental learning, and see that forgetting is not the principal cause of low performance.

Class Incremental Learning Incremental Learning +1

ACAE-REMIND for Online Continual Learning with Compressed Feature Replay

no code implementations18 May 2021 Kai Wang, Luis Herranz, Joost Van de Weijer

Methods are typically allowed to use a limited buffer to store some of the images in the stream.

Continual Learning

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

1 code implementation28 Apr 2021 Yaxing Wang, Abel Gonzalez-Garcia, Chenshen Wu, Luis Herranz, Fahad Shahbaz Khan, Shangling Jui, Joost Van de Weijer

Therefore, we propose a novel knowledge transfer method for generative models based on mining the knowledge that is most beneficial to a specific target domain, either from a single or multiple pretrained GANs.

Transfer Learning

Continual learning in cross-modal retrieval

no code implementations14 Apr 2021 Kai Wang, Luis Herranz, Joost Van de Weijer

We found that the indexing stage pays an important role and that simply avoiding reindexing the database with updated embedding networks can lead to significant gains.

Continual Learning Cross-Modal Retrieval +2

On Implicit Attribute Localization for Generalized Zero-Shot Learning

no code implementations8 Mar 2021 Shiqi Yang, Kai Wang, Luis Herranz, Joost Van de Weijer

Zero-shot learning (ZSL) aims to discriminate images from unseen classes by exploiting relations to seen classes via their attribute-based descriptions.

Attribute Generalized Zero-Shot Learning

Generative Multi-Label Zero-Shot Learning

1 code implementation27 Jan 2021 Akshita Gupta, Sanath Narayan, Salman Khan, Fahad Shahbaz Khan, Ling Shao, Joost Van de Weijer

Nevertheless, computing reliable attention maps for unseen classes during inference in a multi-label setting is still a challenge.

Attribute Generative Adversarial Network +3

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

1 code implementation NeurIPS 2020 Yaxing Wang, Lu Yu, Joost Van de Weijer

To enable the training of deep I2I models on small datasets, we propose a novel transfer learning method, that transfers knowledge from pre-trained GANs.

Attribute Image-to-Image Translation +2

Class-incremental learning: survey and performance evaluation on image classification

1 code implementation28 Oct 2020 Marc Masana, Xialei Liu, Bartlomiej Twardowski, Mikel Menta, Andrew D. Bagdanov, Joost Van de Weijer

For future learning systems, incremental learning is desirable because it allows for: efficient resource usage by eliminating the need to retrain from scratch at the arrival of new data; reduced memory usage by preventing or limiting the amount of data required to be stored -- also important when privacy limitations are imposed; and learning that more closely resembles human learning.

Class Incremental Learning General Classification +2

Casting a BAIT for Offline and Online Source-free Domain Adaptation

2 code implementations23 Oct 2020 Shiqi Yang, Yaxing Wang, Joost Van de Weijer, Luis Herranz, Shangling Jui

When adapting to the target domain, the additional classifier initialized from source classifier is expected to find misclassified features.

Source-Free Domain Adaptation Unsupervised Domain Adaptation

Learning to Rank for Active Learning: A Listwise Approach

no code implementations31 Jul 2020 Minghan Li, Xialei Liu, Joost Van de Weijer, Bogdan Raducanu

Active learning emerged as an alternative to alleviate the effort to label huge amount of data for data hungry applications (such as image/video indexing and retrieval, autonomous driving, etc.).

Active Learning Autonomous Driving +3

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

no code implementations24 Jul 2020 Carola Figueroa-Flores, Bogdan Raducanu, David Berga, Joost Van de Weijer

Most of the saliency methods are evaluated on their ability to generate saliency maps, and not on their functionality in a complete vision pipeline, like for instance, image classification.

Classification Fine-Grained Image Classification +3

RATT: Recurrent Attention to Transient Tasks for Continual Image Captioning

1 code implementation NeurIPS 2020 Riccardo Del Chiaro, Bartłomiej Twardowski, Andrew D. Bagdanov, Joost Van de Weijer

We call our method Recurrent Attention to Transient Tasks (RATT), and also show how to adapt continual learning approaches based on weight egularization and knowledge distillation to recurrent continual learning problems.

Continual Learning Image Captioning +1

On Class Orderings for Incremental Learning

no code implementations4 Jul 2020 Marc Masana, Bartłomiej Twardowski, Joost Van de Weijer

The influence of class orderings in the evaluation of incremental learning has received very little attention.

Incremental Learning

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

no code implementations26 Jun 2020 Kai Wang, Luis Herranz, Anjan Dutta, Joost Van de Weijer

We propose bookworm continual learning(BCL), a flexible setting where unseen classes can be inferred via a semantic model, and the visual model can be updated continually.

Attribute Continual Learning +1

Simple and effective localized attribute representations for zero-shot learning

no code implementations10 Jun 2020 Shiqi Yang, Kai Wang, Luis Herranz, Joost Van de Weijer

Zero-shot learning (ZSL) aims to discriminate images from unseen classes by exploiting relations to seen classes via their semantic descriptions.

Attribute Zero-Shot Learning

Distributed Learning and Inference with Compressed Images

no code implementations22 Apr 2020 Sudeep Katakol, Basem Elbarashy, Luis Herranz, Joost Van de Weijer, Antonio M. Lopez

Moreover, we may only have compressed images at training time but are able to use original images at inference time, or vice versa, and in such a case, the downstream model suffers from covariate shift.

Autonomous Driving Cloud Computing +3

Semantic Drift Compensation for Class-Incremental Learning

2 code implementations CVPR 2020 Lu Yu, Bartłomiej Twardowski, Xialei Liu, Luis Herranz, Kai Wang, Yongmei Cheng, Shangling Jui, Joost Van de Weijer

The vast majority of methods have studied this scenario for classification networks, where for each new task the classification layer of the network must be augmented with additional weights to make room for the newly added classes.

Class Incremental Learning General Classification +1

Ternary Feature Masks: zero-forgetting for task-incremental learning

no code implementations23 Jan 2020 Marc Masana, Tinne Tuytelaars, Joost Van de Weijer

To allow already learned features to adapt to the current task without changing the behavior of these features for previous tasks, we introduce task-specific feature normalization.

Continual Learning Incremental Learning

Learning to adapt class-specific features across domains for semantic segmentation

1 code implementation22 Jan 2020 Mikel Menta, Adriana Romero, Joost Van de Weijer

Recent advances in unsupervised domain adaptation have shown the effectiveness of adversarial training to adapt features across domains, endowing neural networks with the capability of being tested on a target domain without requiring any training annotations in this domain.

Segmentation Semantic Segmentation +2

Variable Rate Deep Image Compression with Modulated Autoencoder

1 code implementation11 Dec 2019 Fei Yang, Luis Herranz, Joost Van de Weijer, José A. Iglesias Guitián, Antonio López, Mikhail Mozerov

Addressing these limitations, we formulate the problem of variable rate-distortion optimization for deep image compression, and propose modulated autoencoders (MAEs), where the representations of a shared autoencoder are adapted to the specific rate-distortion tradeoff via a modulation network.

Image Compression Navigate +1

MineGAN: effective knowledge transfer from GANs to target domains with few images

2 code implementations CVPR 2020 Yaxing Wang, Abel Gonzalez-Garcia, David Berga, Luis Herranz, Fahad Shahbaz Khan, Joost Van de Weijer

We propose a novel knowledge transfer method for generative models based on mining the knowledge that is most beneficial to a specific target domain, either from a single or multiple pretrained GANs.

Transfer Learning

Multi-Modal Fusion for End-to-End RGB-T Tracking

1 code implementation30 Aug 2019 Lichao Zhang, Martin Danelljan, Abel Gonzalez-Garcia, Joost Van de Weijer, Fahad Shahbaz Khan

Our tracker is trained in an end-to-end manner, enabling the components to learn how to fuse the information from both modalities.

Image-to-Image Translation Rgb-T Tracking

Learning the Model Update for Siamese Trackers

1 code implementation ICCV 2019 Lichao Zhang, Abel Gonzalez-Garcia, Joost Van de Weijer, Martin Danelljan, Fahad Shahbaz Khan

In general, this template is linearly combined with the accumulated template from the previous frame, resulting in an exponential decay of information over time.

Visual Tracking

Controlling biases and diversity in diverse image-to-image translation

no code implementations23 Jul 2019 Yaxing Wang, Abel Gonzalez-Garcia, Joost Van de Weijer, Luis Herranz

The task of unpaired image-to-image translation is highly challenging due to the lack of explicit cross-domain pairs of instances.

Image-to-Image Translation Translation

Sparse data interpolation using the geodesic distance affinity space

no code implementations6 May 2019 Mikhail G. Mozerov, Fei Yang, Joost Van de Weijer

In this paper, we adapt the geodesic distance-based recursive filter to the sparse data interpolation problem.

Optical Flow Estimation

Mix and match networks: cross-modal alignment for zero-pair image-to-image translation

no code implementations8 Mar 2019 Yaxing Wang, Luis Herranz, Joost Van de Weijer

This paper addresses the problem of inferring unseen cross-modal image-to-image translations between multiple modalities.

Image-to-Image Translation Segmentation +2

Exploiting Unlabeled Data in CNNs by Self-supervised Learning to Rank

2 code implementations17 Feb 2019 Xialei Liu, Joost Van de Weijer, Andrew D. Bagdanov

Our results show that networks trained to regress to the ground truth targets for labeled data and to simultaneously learn to rank unlabeled data obtain significantly better, state-of-the-art results for both IQA and crowd counting.

Active Learning Crowd Counting +5

One-view occlusion detection for stereo matching with a fully connected CRF model

no code implementations12 Jan 2019 Mikhail G. Mozerov, Joost Van de Weijer

We show that the OVOD approach considerably improves results for cost augmentation and energy minimization techniques in comparison with the standard one-view affinity space implementation.

Stereo Matching Stereo Matching Hand

Deep Spectral Reflectance and Illuminant Estimation from Self-Interreflections

no code implementations9 Dec 2018 Rada Deeb, Joost Van de Weijer, Damien Muselet, Mathieu Hebert, Alain Tremeau

In this work, we propose a CNN-based approach to estimate the spectral reflectance of a surface and the spectral power distribution of the light from a single RGB image of a V-shaped surface.

Memory Replay GANs: Learning to Generate New Categories without Forgetting

1 code implementation NeurIPS 2018 Chenshen Wu, Luis Herranz, Xialei Liu, Yaxing Wang, Joost Van de Weijer, Bogdan Raducanu

In particular, we investigate generative adversarial networks (GANs) in the task of learning new categories in a sequential fashion.

Memory Replay GANs: learning to generate images from new categories without forgetting

1 code implementation6 Sep 2018 Chenshen Wu, Luis Herranz, Xialei Liu, Yaxing Wang, Joost Van de Weijer, Bogdan Raducanu

In particular, we investigate generative adversarial networks (GANs) in the task of learning new categories in a sequential fashion.

LIUM-CVC Submissions for WMT18 Multimodal Translation Task

no code implementations WS 2018 Ozan Caglayan, Adrien Bardet, Fethi Bougares, Loïc Barrault, Kai Wang, Marc Masana, Luis Herranz, Joost Van de Weijer

This paper describes the multimodal Neural Machine Translation systems developed by LIUM and CVC for WMT18 Shared Task on Multimodal Translation.

Machine Translation Translation

Metric Learning for Novelty and Anomaly Detection

1 code implementation16 Aug 2018 Marc Masana, Idoia Ruiz, Joan Serrat, Joost Van de Weijer, Antonio M. Lopez

When neural networks process images which do not resemble the distribution seen during training, so called out-of-distribution images, they often make wrong predictions, and do so too confidently.

Anomaly Detection Metric Learning +3

Context Proposals for Saliency Detection

no code implementations27 Jun 2018 Aymen Azaza, Joost Van de Weijer, Ali Douik, Marc Masana

Therefore, we extend object proposal methods with context proposals, which allow to incorporate the immediate context in the saliency computation.

Object object-detection +3

Synthetic data generation for end-to-end thermal infrared tracking

no code implementations4 Jun 2018 Lichao Zhang, Abel Gonzalez-Garcia, Joost Van de Weijer, Martin Danelljan, Fahad Shahbaz Khan

These methods provide us with a large labeled dataset of synthetic TIR sequences, on which we can train end-to-end optimal features for tracking.

Image-to-Image Translation Synthetic Data Generation +2

Image-to-image translation for cross-domain disentanglement

1 code implementation NeurIPS 2018 Abel Gonzalez-Garcia, Joost Van de Weijer, Yoshua Bengio

We compare our model to the state-of-the-art in multi-modal image translation and achieve better results for translation on challenging datasets as well as for cross-domain retrieval on realistic datasets.

Disentanglement Image-to-Image Translation +2

Weakly Supervised Domain-Specific Color Naming Based on Attention

1 code implementation11 May 2018 Lu Yu, Yongmei Cheng, Joost Van de Weijer

The attention branch is used to modulate the pixel-wise color naming predictions of the network.

General Classification

Transferring GANs: generating images from limited data

1 code implementation ECCV 2018 Yaxing Wang, Chenshen Wu, Luis Herranz, Joost Van de Weijer, Abel Gonzalez-Garcia, Bogdan Raducanu

Transferring the knowledge of pretrained networks to new domains by means of finetuning is a widely used practice for applications based on discriminative models.

10-shot image generation Domain Adaptation +1

Mix and match networks: encoder-decoder alignment for zero-pair image translation

1 code implementation CVPR 2018 Yaxing Wang, Joost Van de Weijer, Luis Herranz

We address the problem of image translation between domains or modalities for which no direct paired data is available (i. e. zero-pair translation).

Colorization Segmentation +3

Leveraging Unlabeled Data for Crowd Counting by Learning to Rank

1 code implementation CVPR 2018 Xialei Liu, Joost Van de Weijer, Andrew D. Bagdanov

We propose a novel crowd counting approach that leverages abundantly available unlabeled crowd imagery in a learning-to-rank framework.

Crowd Counting Image Retrieval +2

Rotate your Networks: Better Weight Consolidation and Less Catastrophic Forgetting

2 code implementations8 Feb 2018 Xialei Liu, Marc Masana, Luis Herranz, Joost Van de Weijer, Antonio M. Lopez, Andrew D. Bagdanov

In this paper we propose an approach to avoiding catastrophic forgetting in sequential task learning scenarios.

Domain-adaptive deep network compression

2 code implementations ICCV 2017 Marc Masana, Joost Van de Weijer, Luis Herranz, Andrew D. Bagdanov, Jose M. Alvarez

We show that domain transfer leads to large shifts in network activations and that it is desirable to take this into account when compressing.

Low-rank compression

Adversarial Networks for Spatial Context-Aware Spectral Image Reconstruction from RGB

no code implementations1 Sep 2017 Aitor Alvarez-Gila, Joost Van de Weijer, Estibaliz Garrote

Hyperspectral signal reconstruction aims at recovering the original spectral input that produced a certain trichromatic (RGB) response from a capturing device or observer.

Image Reconstruction

Review on Computer Vision Techniques in Emergency Situation

no code implementations24 Aug 2017 Laura Lopez-Fuentes, Joost Van de Weijer, Manuel Gonzalez-Hidalgo, Harald Skinnemoen, Andrew D. Bagdanov

The number of emergencies where computer vision tools has been considered or used is very wide, and there is a great overlap across related emergency research.

RankIQA: Learning from Rankings for No-reference Image Quality Assessment

2 code implementations ICCV 2017 Xialei Liu, Joost Van de Weijer, Andrew D. Bagdanov

Furthermore, on the LIVE benchmark we show that our approach is superior to existing NR-IQA techniques and that we even outperform the state-of-the-art in full-reference IQA (FR-IQA) methods without having to resort to high-quality reference images to infer IQA.

No-Reference Image Quality Assessment NR-IQA

LIUM-CVC Submissions for WMT17 Multimodal Translation Task

no code implementations WS 2017 Ozan Caglayan, Walid Aransa, Adrien Bardet, Mercedes García-Martínez, Fethi Bougares, Loïc Barrault, Marc Masana, Luis Herranz, Joost Van de Weijer

This paper describes the monomodal and multimodal Neural Machine Translation systems developed by LIUM and CVC for WMT17 Shared Task on Multimodal Translation.

Machine Translation Translation

Binary Patterns Encoded Convolutional Neural Networks for Texture Recognition and Remote Sensing Scene Classification

no code implementations5 Jun 2017 Rao Muhammad Anwer, Fahad Shahbaz Khan, Joost Van de Weijer, Matthieu Molinier, Jorma Laaksonen

To the best of our knowledge, we are the first to investigate Binary Patterns encoded CNNs and different deep network fusion architectures for texture recognition and remote sensing scene classification.

Aerial Scene Classification General Classification +2

Bandwidth limited object recognition in high resolution imagery

no code implementations16 Jan 2017 Laura Lopez-Fuentes, Andrew D. Bagdanov, Joost Van de Weijer, Harald Skinnemoen

This paper proposes a novel method to optimize bandwidth usage for object detection in critical communication scenarios.

Object object-detection +3

Scale Coding Bag of Deep Features for Human Attribute and Action Recognition

no code implementations14 Dec 2016 Fahad Shahbaz Khan, Joost Van de Weijer, Rao Muhammad Anwer, Andrew D. Bagdanov, Michael Felsberg, Jorma Laaksonen

Most approaches to human attribute and action recognition in still images are based on image representation in which multi-scale local features are pooled across scale into a single, scale-invariant encoding.

Action Recognition In Still Images Attribute

Ensembles of Generative Adversarial Networks

no code implementations3 Dec 2016 Yaxing Wang, Lichao Zhang, Joost Van de Weijer

The first one is based on the fact that in the minimax game which is played to optimize the GAN objective the generator network keeps on changing even after the network can be considered optimal.

Invertible Conditional GANs for image editing

6 code implementations19 Nov 2016 Guim Perarnau, Joost Van de Weijer, Bogdan Raducanu, Jose M. Álvarez

Generative Adversarial Networks (GANs) have recently demonstrated to successfully approximate complex data distributions.

Conditional Image Generation Image-to-Image Translation

On-the-fly Network Pruning for Object Detection

no code implementations11 May 2016 Marc Masana, Joost Van de Weijer, Andrew D. Bagdanov

Object detection with deep neural networks is often performed by passing a few thousand candidate bounding boxes through a deep neural network for each image.

Network Pruning Object +2

From Emotions to Action Units With Hidden and Semi-Hidden-Task Learning

no code implementations ICCV 2015 Adria Ruiz, Joost Van de Weijer, Xavier Binefa

Additionally, we show that SHTL achieves competitive performance compared with state-of-the-art Transductive Learning approaches which face the problem of limited training data by using unlabelled test samples during training.

Transductive Learning

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