1.4k citations · 2.6k across the 155 of their papers we have counts for
18 papers · 2 filters
ComboGAN: Unrestrained Scalability for Image Domain Translation
Asha Anoosheh, Eirikur Agustsson, Radu Timofte +1
This year alone has seen unprecedented leaps in the area of learning-based image translation, namely CycleGAN, by Zhu et al. But experiments so far have been tailored to merely two…
Logo Synthesis and Manipulation with Clustered Generative Adversarial Networks
Alexander Sage, Eirikur Agustsson, Radu Timofte +1
Designing a logo for a new brand is a lengthy and tedious back-and-forth process between a designer and a client. In this paper we explore to what extent machine learning can solve…
Object Referring in Visual Scene with Spoken Language
Arun Balajee Vasudevan, Dengxin Dai, Luc Van Gool
Object referring has important applications, especially for human-machine interaction. While having received great attention, the task is mainly attacked with written language (tex…
Manifold-valued Image Generation with Wasserstein Generative Adversarial Nets
Zhiwu Huang, Jiqing Wu, Luc Van Gool
Generative modeling over natural images is one of the most fundamental machine learning problems. However, few modern generative models, including Wasserstein Generative Adversaria…
Wasserstein Divergence for GANs
Jiqing Wu, Zhiwu Huang, Janine Thoma +2
In many domains of computer vision, generative adversarial networks (GANs) have achieved great success, among which the family of Wasserstein GANs (WGANs) is considered to be state…
Detection-aided liver lesion segmentation using deep learning
Miriam Bellver, Kevis-Kokitsi Maninis, Jordi Pont-Tuset +3
A fully automatic technique for segmenting the liver and localizing its unhealthy tissues is a convenient tool in order to diagnose hepatic diseases and assess the response to the…
Deep Extreme Cut: From Extreme Points to Object Segmentation
Kevis-Kokitsi Maninis, Sergi Caelles, Jordi Pont-Tuset +1
This paper explores the use of extreme points in an object (left-most, right-most, top, bottom pixels) as input to obtain precise object segmentation for images and videos. We do s…
Video Object Segmentation Without Temporal Information
Kevis-Kokitsi Maninis, Sergi Caelles, Yuhua Chen +4
Video Object Segmentation, and video processing in general, has been historically dominated by methods that rely on the temporal consistency and redundancy in consecutive video fra…
Automatic Tool Landmark Detection for Stereo Vision in Robot-Assisted Retinal Surgery
Thomas Probst, Kevis-Kokitsi Maninis, Ajad Chhatkuli +3
Computer vision and robotics are being increasingly applied in medical interventions. Especially in interventions where extreme precision is required they could make a difference.…
WESPE: Weakly Supervised Photo Enhancer for Digital Cameras
Andrey Ignatov, Nikolay Kobyshev, Radu Timofte +2
Low-end and compact mobile cameras demonstrate limited photo quality mainly due to space, hardware and budget constraints. In this work, we propose a deep learning solution that tr…
Semantic Foggy Scene Understanding with Synthetic Data
Christos Sakaridis, Dengxin Dai, Luc Van Gool
This work addresses the problem of semantic foggy scene understanding (SFSU). Although extensive research has been performed on image dehazing and on semantic scene understanding w…
The WILDTRACK Multi-Camera Person Dataset
Tatjana Chavdarova, Pierre Baqué, Stéphane Bouquet +6
People detection methods are highly sensitive to the perpetual occlusions among the targets. As multi-camera set-ups become more frequently encountered, joint exploitation of the a…
Temporal Segment Networks for Action Recognition in Videos
Limin Wang, Yuanjun Xiong, Zhe Wang +4
Deep convolutional networks have achieved great success for image recognition. However, for action recognition in videos, their advantage over traditional methods is not so evident…
On the Relation between Color Image Denoising and Classification
Jiqing Wu, Radu Timofte, Zhiwu Huang +1
Large amount of image denoising literature focuses on single channel images and often experimentally validates the proposed methods on tens of images at most. In this paper, we inv…
DSLR-Quality Photos on Mobile Devices with Deep Convolutional Networks
Andrey Ignatov, Nikolay Kobyshev, Radu Timofte +2
Despite a rapid rise in the quality of built-in smartphone cameras, their physical limitations - small sensor size, compact lenses and the lack of specific hardware, - impede them…
Semantically-Guided Video Object Segmentation
Sergi Caelles, Yuhua Chen, Jordi Pont-Tuset +1
This paper tackles the problem of semi-supervised video object segmentation, that is, segmenting an object in a sequence given its mask in the first frame. One of the main challeng…
Crossing Nets: Combining GANs and VAEs with a Shared Latent Space for Hand Pose Estimation
Chengde Wan, Thomas Probst, Luc Van Gool +1
State-of-the-art methods for 3D hand pose estimation from depth images require large amounts of annotated training data. We propose to model the statistical relationships of 3D han…
Convolutional Oriented Boundaries: From Image Segmentation to High-Level Tasks
Kevis-Kokitsi Maninis, Jordi Pont-Tuset, Pablo Arbeláez +1
We present Convolutional Oriented Boundaries (COB), which produces multiscale oriented contours and region hierarchies starting from generic image classification Convolutional Neur…