activity
20172019
most citedScene recognition with CNNs: objects, scales and dataset bias

160 citations · 221 across the 4 of their papers we have counts for

collaborators

5 papers

cs.CV20193 cited

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

Yaxing Wang, Abel Gonzalez-Garcia, Joost van de Weijer +1

The task of unpaired image-to-image translation is highly challenging due to the lack of explicit cross-domain pairs of instances. We consider here diverse image translation (DIT),…

cs.CV201815 cited

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

Luis Herranz, Weiqing Min, Shuqiang Jiang

The central role of food in our individual and social life, combined with recent technological advances, has motivated a growing interest in applications that help to better monito…

cs.CV2018160 cited

Scene recognition with CNNs: objects, scales and dataset bias

Luis Herranz, Shuqiang Jiang, Xiangyang Li

Since scenes are composed in part of objects, accurate recognition of scenes requires knowledge about both scenes and objects. In this paper we address two related problems: 1) sca…

cs.CV201843 cited

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

Xinhang Song, Luis Herranz, Shuqiang Jiang

Scene recognition with RGB images has been extensively studied and has reached very remarkable recognition levels, thanks to convolutional neural networks (CNN) and large scene dat…

cs.CL2017

LIUM-CVC Submissions for WMT17 Multimodal Translation Task

Ozan Caglayan, Walid Aransa, Adrien Bardet +6

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