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Luis Herranz

17 accepted papers

2025

Revisiting Image Fusion for Multi-Illuminant White-Balance Correction

David Serrano-Lozano, Aditya Arora, Luis Herranz, Konstantinos G. Derpanis, Michael S. Brown, Javier Vazquez-Corral
ICCV 2025poster

White balance (WB) correction in scenes with multiple illuminants remains a persistent challenge in computer vision. Recent methods explored fusion-based approaches, where a neural network linearly blends multiple sRGB versions of an input image, each processed with predefined WB presets. However, w…

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2024

Learned Image Enhancement via Color Naming

David Serrano-Lozano*, Luis Herranz, Michael S Brown, Javier Vazquez-Corral
ECCV 2024poster

"A popular method for enhancing images involves learning the style of a professional photo editor using pairs of training images comprised of the original input with the editor-enhanced version. When manipulating images, many editing tools offer a feature that allows the user to manipulate a limited…

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2023

Burst Perception-Distortion Tradeoff: Analysis and Evaluation

Danna Xue, Luis Herranz, Javier Vazquez-Corral, Yanning Zhang
ICASSP 2023accepted

Burst image restoration attempts to effectively utilize the complementary cues appearing in sequential images to produce a high-quality image. Most current methods use all the available images to obtain the reconstructed image. However, using more images for burst restoration is not always the best…

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2023

Efficient Super-Resolution for Compression Of Gaming Videos

Yifan Wang, Luka Murn, Luis Herranz, Fei Yang, Marta Mrak, Wei Zhang +2
ICASSP 2023accepted

Due to the increasing demand for game-streaming services, efficient compression of computer-generated video is more critical than ever, especially when the available bandwidth is low. This paper proposes a super-resolution framework that improves the coding efficiency of computer-generated gaming vi…

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2023

Semantic Preprocessor for Image Compression for Machines

Mingyi Yang, Luis Herranz, Fei Yang, Luka Murn, Marc Górriz Blanch, Shuai Wan +2
ICASSP 2023accepted

Visual content is being increasingly transmitted and consumed by machines rather than humans to perform automated content analysis tasks. In this paper, we propose an image preprocessor that optimizes the input image for machine consumption prior to encoding by an off-the-shelf codec designed for hu…

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2022

DCNGAN: A Deformable Convolution-Based GAN with QP Adaptation for Perceptual Quality Enhancement of Compressed Video

Saiping Zhang, Luis Herranz, Marta Mrak, Marc Górriz Blanch, Shuai Wan, Fuzheng Yang
ICASSP 2022accepted

In this paper, we propose a deformable convolution-based generative adversarial network (DCNGAN) for perceptual quality enhancement of compressed videos. DCNGAN is also adaptive to the quantization parameters (QPs). Compared with optical flows, deformable convolutions are more effective and efficien…

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2021

Exploiting the Intrinsic Neighborhood Structure for Source-free Domain Adaptation

Shiqi Yang, Yaxing Wang, Joost van de weijer, Luis Herranz, SHANGLING JUI
NeurIPS 2021poster

Domain adaptation (DA) aims to alleviate the domain shift between source domain and target domain. Most DA methods require access to the source data, but often that is not possible (e.g. due to data privacy or intellectual property). In this paper, we address the challenging source-free domain adapt…

Cited by 308Source ↗PDF ↗code ↗Scholar ↗
2021

Generalized Source-Free Domain Adaptation

Shiqi Yang, Yaxing Wang, Joost van de Weijer, Luis Herranz, Shangling Jui
ICCV 2021poster

Domain adaptation (DA) aims to transfer the knowledge learned from source domain to an unlabeled target domain. Some recent works tackle source-free domain adaptation (SFDA) where only source pre-trained model is available for adaptation to target domain. However those methods does not consider keep…

Cited by 324PDF ↗code ↗Scholar ↗
2021

Slimmable Compressive Autoencoders for Practical Neural Image Compression

Fei Yang, Luis Herranz, Yongmei Cheng, Mikhail G. Mozerov
CVPR 2021poster

Neural image compression leverages deep neural networks to outperform traditional image codecs in rate-distortion performance. However, the resulting models are also heavy, computationally demanding and generally optimized for a single rate, limiting their practical use. Focusing on practical image…

Cited by 94PDF ↗code ↗Scholar ↗
2020

MineGAN: Effective Knowledge Transfer From GANs to Target Domains With Few Images

Yaxing Wang, Abel Gonzalez-Garcia, David Berga, Luis Herranz, Fahad Shahbaz Khan, Joost van de Weijer
CVPR 2020poster

One of the attractive characteristics of deep neural networks is their ability to transfer knowledge obtained in one domain to other related domains. As a result, high-quality networks can be trained in domains with relatively little training data. This property has been extensively studied for disc…

Cited by 229PDF ↗code ↗Scholar ↗
2020

Semantic Drift Compensation for Class-Incremental Learning

Lu Yu, Bartlomiej Twardowski, Xialei Liu, Luis Herranz, Kai Wang, Yongmei Cheng +2
CVPR 2020poster

Class-incremental learning of deep networks sequentially increases the number of classes to be classified. During training, the network has only access to data of one task at a time, where each task contains several classes. In this setting, networks suffer from catastrophic forgetting which refers…

Cited by 413PDF ↗code ↗Scholar ↗
2018

Memory Replay GANs: Learning to Generate New Categories without Forgetting

Chenshen Wu, Luis Herranz, Xialei Liu, yaxing wang, Joost van de Weijer, Bogdan Raducanu
NeurIPS 2018poster

Previous works on sequential learning address the problem of forgetting in discriminative models. In this paper we consider the case of generative models. In particular, we investigate generative adversarial networks (GANs) in the task of learning new categories in a sequential fashion. We first sho…

Cited by 540Source ↗PDF ↗code ↗Scholar ↗
2018

Mix and Match Networks: Encoder-Decoder Alignment for Zero-Pair Image Translation

Yaxing Wang, Joost van de Weijer, Luis Herranz
CVPR 2018poster

We address the problem of image translation between domains or modalities for which no direct paired data is available (i.e. zero-pair translation). We propose mix and match networks, based on multiple encoders and decoders aligned in such a way that other encoder-decoder pairs can be composed at te…

Cited by 36Source ↗PDF ↗code ↗Scholar ↗
2018

Transferring GANs: generating images from limited data

Yaxing Wang, Chenshen Wu, Luis Herranz, Joost van de Weijer, Abel Gonzalez-Garcia, Bogdan Raducanu
ECCV 2018poster

Transferring the knowledge of pretrained networks to new domains by means of finetuning is a widely used practice for applications based on discriminative models. To the best of our knowledge this practice has not been studied within the context of generative deep networks. Therefore, we study domai…

Cited by 393Source ↗PDF ↗code ↗Scholar ↗
2017

Domain-Adaptive Deep Network Compression

Marc Masana, Joost van de Weijer, Luis Herranz, Andrew D. Bagdanov, Jose M. Alvarez
ICCV 2017poster

Deep Neural Networks trained on large datasets can be easily transferred to new domains with far fewer labeled examples by a process called fine-tuning. This has the advantage that representations learned in the large source domain can be exploited on smaller target domains. However, networks design…

Cited by 80PDF ↗code ↗Scholar ↗
2015

Joint Multi-Feature Spatial Context for Scene Recognition on the Semantic Manifold

Xinhang Song, Shuqiang Jiang, Luis Herranz
CVPR 2015poster

In the semantic multinomial framework patches and images are modeled as points in a semantic probability simplex. Patch theme models are learned resorting to weak supervision via image labels, which leads the problem of scene categories co-occurring in this semantic space. Fortunately, each category…

Cited by 38Source ↗PDF ↗Scholar ↗