20 citations · 27 across the 4 of their papers we have counts for
9 papers
Retrieval Guided Unsupervised Multi-domain Image-to-Image Translation
Raul Gomez, Yahui Liu, Marco De Nadai +3
Image to image translation aims to learn a mapping that transforms an image from one visual domain to another. Recent works assume that images descriptors can be disentangled into…
Location Sensitive Image Retrieval and Tagging
Raul Gomez, Jaume Gibert, Lluis Gomez +1
People from different parts of the globe describe objects and concepts in distinct manners. Visual appearance can thus vary across different geographic locations, which makes locat…
Exploring Hate Speech Detection in Multimodal Publications
Raul Gomez, Jaume Gibert, Lluis Gomez +1
In this work we target the problem of hate speech detection in multimodal publications formed by a text and an image. We gather and annotate a large scale dataset from Twitter, MMH…
Selective Style Transfer for Text
Raul Gomez, Ali Furkan Biten, Lluis Gomez +3
This paper explores the possibilities of image style transfer applied to text maintaining the original transcriptions. Results on different text domains (scene text, machine printe…
Self-Supervised Learning from Web Data for Multimodal Retrieval
Raul Gomez, Lluis Gomez, Jaume Gibert +1
Self-Supervised learning from multimodal image and text data allows deep neural networks to learn powerful features with no need of human annotated data. Web and Social Media platf…
Learning from #Barcelona Instagram data what Locals and Tourists post about its Neighbourhoods
Raul Gomez, Lluis Gomez, Jaume Gibert +1
Massive tourism is becoming a big problem for some cities, such as Barcelona, due to its concentration in some neighborhoods. In this work we gather Instagram data related to Barce…
Learning to Learn from Web Data through Deep Semantic Embeddings
Raul Gomez, Lluis Gomez, Jaume Gibert +1
In this paper we propose to learn a multimodal image and text embedding from Web and Social Media data, aiming to leverage the semantic knowledge learnt in the text domain and tran…
TextTopicNet - Self-Supervised Learning of Visual Features Through Embedding Images on Semantic Text Spaces
Yash Patel, Lluis Gomez, Raul Gomez +3
The immense success of deep learning based methods in computer vision heavily relies on large scale training datasets. These richly annotated datasets help the network learn discri…
Improving Text Proposals for Scene Images with Fully Convolutional Networks
Dena Bazazian, Raul Gomez, Anguelos Nicolaou +3
Text Proposals have emerged as a class-dependent version of object proposals - efficient approaches to reduce the search space of possible text object locations in an image. Combin…