37 citations · 68 across the 9 of their papers we have counts for
9 papers
Recognizing Emotions evoked by Movies using Multitask Learning
Hassan Hayat, Carles Ventura, Agata Lapedriza
Understanding the emotional impact of movies has become important for affective movie analysis, ranking, and indexing. Methods for recognizing evoked emotions are usually trained o…
SynthRef: Generation of Synthetic Referring Expressions for Object Segmentation
Ioannis Kazakos, Carles Ventura, Miriam Bellver +2
Recent advances in deep learning have brought significant progress in visual grounding tasks such as language-guided video object segmentation. However, collecting large datasets f…
RefVOS: A Closer Look at Referring Expressions for Video Object Segmentation
Miriam Bellver, Carles Ventura, Carina Silberer +3
The task of video object segmentation with referring expressions (language-guided VOS) is to, given a linguistic phrase and a video, generate binary masks for the object to which t…
Curriculum Learning for Recurrent Video Object Segmentation
Maria Gonzalez-i-Calabuig, Carles Ventura, Xavier Giró-i-Nieto
Video object segmentation can be understood as a sequence-to-sequence task that can benefit from the curriculum learning strategies for better and faster training of deep neural ne…
Recurrent Instance Segmentation using Sequences of Referring Expressions
Alba Herrera-Palacio, Carles Ventura, Carina Silberer +3
The goal of this work is to segment the objects in an image that are referred to by a sequence of linguistic descriptions (referring expressions). We propose a deep neural network…
RVOS: End-to-End Recurrent Network for Video Object Segmentation
Carles Ventura, Miriam Bellver, Andreu Girbau +3
Multiple object video object segmentation is a challenging task, specially for the zero-shot case, when no object mask is given at the initial frame and the model has to find the o…
Iterative Deep Learning for Road Topology Extraction
Carles Ventura, Jordi Pont-Tuset, Sergi Caelles +2
This paper tackles the task of estimating the topology of road networks from aerial images. Building on top of a global model that performs a dense semantical classification of the…
Iterative Deep Learning for Network Topology Extraction
Carles Ventura, Jordi Pont-Tuset, Sergi Caelles +2
This paper tackles the task of estimating the topology of filamentary networks such as retinal vessels and road networks. Building on top of a global model that performs a dense se…
Improving Spatial Codification in Semantic Segmentation
Carles Ventura, Xavier Giró-i-Nieto, Verónica Vilaplana +3
This paper explores novel approaches for improving the spatial codification for the pooling of local descriptors to solve the semantic segmentation problem. We propose to partition…