Ponències/Comunicacions de congressos
Recent Submissions
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Refinement network for unsupervised on the scene foreground segmentation
(European Association for Signal Processing (EURASIP), 2020)
Conference report
Open AccessUnsupervised learning represents one of the most interesting challenges in computer vision today. The task has an immense practical value with many applications in artificial intelligence and emerging technologies, as large ... -
Explore, discover and learn: unsupervised discovery of state-covering skills
(2020)
Conference lecture
Open AccessAcquiring abilities in the absence of a task-oriented reward function is at the frontier of reinforcement learning research. This problem has been studied through the lens of empowerment, which draws a connection between ... -
Weakly supervised semantic segmentation for remote sensing hyperspectral imaging
(Institute of Electrical and Electronics Engineers (IEEE), 2020)
Conference lecture
Restricted access - publisher's policyThis paper studies the problem of training a semantic segmentation neural network with weak annotations, in order to be applied in aerial vegetation images from Teide National Park. It proposes a Deep Seeded Region Growing ... -
One perceptron to rule them all: language, vision, audio and speech
(Association for Computing Machinery (ACM), 2020)
Conference lecture
Restricted access - publisher's policyDeep neural networks have boosted the convergence of multimedia data analytics in a unified framework shared by practitioners in natural language, vision and speech. Image captioning, lip reading or video sonorization are ... -
Automatic reminiscence therapy for dementia
(Association for Computing Machinery (ACM), 2020)
Conference lecture
Restricted access - publisher's policyWith people living longer than ever, the number of cases with dementia such as Alzheimer's disease increases steadily. It affects more than 46 million people worldwide, and it is estimated that in 2050 more than 100 million ... -
Audience measurement using a top-view camera and oriented trajectories
(2019)
Conference report
Restricted access - publisher's policyA crucial aspect for selecting optimal areas for commercial advertising is the probability with which that publicity will be seen. This paper presents a method based on top-view camera measurement, where the probability ... -
VLX-Stories: building an online Event Knowledge Base with Emerging Entity detection
(Springer, 2019)
Conference lecture
Restricted access - publisher's policyWe present an online multilingual system for event detection and comprehension from media feeds. The system retrieves information from news sites, aggregates them into events (event detection), and summarizes them by ... -
Budget-aware semi-supervised semantic and instance segmentation
(2019)
Conference lecture
Open AccessMethods that move towards less supervised scenarios are key for image segmentation, as dense labels demand significant human intervention. Generally, the annotation burden is mitigated by labeling datasets with weaker forms ... -
Residual attention graph convolutional network for geometric 3D scene classification
(Institute of Electrical and Electronics Engineers (IEEE), 2019)
Conference report
Restricted access - publisher's policyGeometric 3D scene classification is a very challenging task. Current methodologies extract the geometric information using only a depth channel provided by an RGB-D sensor. These kinds of methodologies introduce possible ... -
VLX-Stories: a semantically linked event platform for media publishers
(CEUR-WS.org, 2019)
Conference lecture
Open AccessIn the recent years, video sharing in social media from different video recording devices has resulted in a exponential growth of videos on the Internet. Such video data is continuously increasing with daily recordings ... -
Hyperparameter-free losses for model-based monocular reconstruction
(Institute of Electrical and Electronics Engineers (IEEE), 2019)
Conference lecture
Open AccessThis work proposes novel hyperparameter-free losses for single view 3D reconstruction with morphable models (3DMM). We dispense with the hyperparameters used in other works by exploiting geometry, so that the shape of the ... -
Picking groups instead of samples: a close look at Static Pool-based Meta-Active Learning
(Institute of Electrical and Electronics Engineers (IEEE), 2019)
Conference lecture
Open AccessActive Learning techniques are used to tackle learning problems where obtaining training labels is costly. In this work we use Meta-Active Learning to learn to select a subset of samples from a pool of unsupervised input ...
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