b'Luis Herranz'

research

∙ 07/13/2022

SlimSeg: Slimmable Semantic Segmentation with Boundary Supervision

Accurate semantic segmentation models typically require significant comp...

Danna Xue, et al. ∙

research

∙ 06/16/2022

PeQuENet: Perceptual Quality Enhancement of Compressed Video with Adaptation- and Attention-based Network

In this paper we propose a generative adversarial network (GAN) framewor...

Saiping Zhang, et al. ∙

research

∙ 05/13/2022

Slimmable Video Codec

Neural video compression has emerged as a novel paradigm combining train...

Zhaocheng Liu, 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 ...

Vacit Oguz Yazici, et al. ∙

research

∙ 01/22/2022

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

In this paper, we propose a deformable convolution-based generative adve...

Saiping Zhang, et al. ∙

research

∙ 11/25/2021

A Novel Framework for Image-to-image Translation and Image Compression

Data-driven paradigms using machine learning are becoming ubiquitous in ...

Fei Yang, 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...

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...

Kai Wang, 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...

Shiqi Yang, et al. ∙

research

∙ 09/24/2021

Spatial Information Refinement for Chroma Intra Prediction in Video Coding

Video compression benefits from advanced chroma intra prediction methods...

Chengyi Zou, et al. ∙

research

∙ 09/22/2021

DVC-P: Deep Video Compression with Perceptual Optimizations

Recent years have witnessed the significant development of learning-base...

Saiping Zhang, et al. ∙

research

∙ 08/03/2021

Generalized Source-free Domain Adaptation

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

Shiqi Yang, 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...

Kai 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...

Yaxing Wang, et al. ∙

research

∙ 04/19/2021

DANICE: Domain adaptation without forgetting in neural image compression

Neural image compression (NIC) is a new coding paradigm where coding cap...

Sudeep Katakol, et al. ∙

research

∙ 04/14/2021

Continual learning in cross-modal retrieval

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

Kai Wang, et al. ∙

research

∙ 03/29/2021

Slimmable Compressive Autoencoders for Practical Neural Image Compression

Neural image compression leverages deep neural networks to outperform tr...

Fei Yang, 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...

Shiqi Yang, 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...

Shiqi Yang, 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...

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...

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...

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...

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...

Lu Yu, et al. ∙

research

∙ 12/11/2019

Variable Rate Deep Image Compression with Modulated Autoencoder

Variable rate is a requirement for flexible and adaptable image and vide...

Fei Yang, et al. ∙

research

∙ 12/11/2019

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

One of the attractive characteristics of deep neural networks is their a...

Yaxing Wang, et al. ∙

research

∙ 08/19/2019

SDIT: Scalable and Diverse Cross-domain Image Translation

Recently, image-to-image translation research has witnessed remarkable p...

Yaxing Wang, et al. ∙

research

∙ 07/23/2019

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

The task of unpaired image-to-image translation is highly challenging du...

Yaxing Wang, et al. ∙

research

∙ 07/11/2019

Multifaceted Analysis of Fine-Tuning in Deep Model for Visual Recognition

In recent years, convolutional neural networks (CNNs) have achieved impr...

Xiangyang Li, et al. ∙

research

∙ 03/08/2019

Mix and match networks: multi-domain alignment for unpaired image-to-image translation

This paper addresses the problem of inferring unseen cross-domain and cr...

Yaxing Wang, et al. ∙

research

∙ 12/01/2018

Cross-Modulation Networks for Few-Shot Learning

A family of recent successful approaches to few-shot learning relies on ...

Hugo Prol, et al. ∙

research

∙ 09/17/2018

Learning Effective RGB-D Representations for Scene Recognition

Deep convolutional networks (CNN) can achieve impressive results on RGB ...

Xinhang Song, et al. ∙

research

∙ 09/06/2018

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

Previous works on sequential learning address the problem of forgetting ...

Chenshen Wu, et al. ∙

research

∙ 09/01/2018

LIUM-CVC Submissions for WMT18 Multimodal Translation Task

This paper describes the multimodal Neural Machine Translation systems d...

Ozan Caglayan, et al. ∙

research

∙ 05/04/2018

Transferring GANs: generating images from limited data

Transferring the knowledge of pretrained networks to new domains by mean...

Yaxing Wang, et al. ∙

research

∙ 04/06/2018

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

We address the problem of image translation between domains or modalitie...

Yaxing Wang, et al. ∙

research

∙ 02/08/2018

Rotate your Networks: Better Weight Consolidation and Less Catastrophic Forgetting

In this paper we propose an approach to avoiding catastrophic forgetting...

Xialei Liu, et al. ∙

research

∙ 01/22/2018

Food recognition and recipe analysis: integrating visual content, context and external knowledge

The central role of food in our individual and social life, combined wit...

Luis Herranz, et al. ∙

research

∙ 01/21/2018

Scene recognition with CNNs: objects, scales and dataset bias

Since scenes are composed in part of objects, accurate recognition of sc...

Luis Herranz, et al. ∙

research

∙ 01/21/2018

Depth CNNs for RGB-D scene recognition: learning from scratch better than transferring from RGB-CNNs

Scene recognition with RGB images has been extensively studied and has r...

Xinhang Song, et al. ∙

research

∙ 09/04/2017

Domain-adaptive deep network compression

Deep Neural Networks trained on large datasets can be easily transferred...

Marc Masana, et al. ∙

research

∙ 07/14/2017

LIUM-CVC Submissions for WMT17 Multimodal Translation Task

This paper describes the monomodal and multimodal Neural Machine Transla...

Ozan Caglayan, et al. ∙

Luis Herranz

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