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Found 19 papers, 16 papers with code
Tackling Low-Resourced Sign Language Translation: UPC at WMT-SLT 22
This paper describes the system developed at the Universitat Polit\`ecnica de Catalunya for the Workshop on Machine Translation 2022 Sign Language Translation Task, in particular, for the sign-to-text direction.
Ranked #1 on
SRF (test)
on WMT-SLT22
Topic Detection in Continuous Sign Language Videos
Significant progress has been made recently on challenging tasks in automatic sign language understanding, such as sign language recognition, translation and production.
Distributing Deep Learning Hyperparameter Tuning for 3D Medical Image Segmentation
However, experiment parallelism is also an option, where different training processes are parallelized across resources.
RefVOS: A Closer Look at Referring Expressions for Video Object Segmentation
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 the phrase refers.
Ranked #1 on
Referring Expression Segmentation
on A2Dre test
Mask-guided sample selection for Semi-Supervised Instance Segmentation
Our method consists in first predicting pseudo-masks for the unlabeled pool of samples, together with a score predicting the quality of the mask.
How2Sign: A Large-scale Multimodal Dataset for Continuous American Sign Language
Towards this end, we introduce How2Sign, a multimodal and multiview continuous American Sign Language (ASL) dataset, consisting of a parallel corpus of more than 80 hours of sign language videos and a set of corresponding modalities including speech, English transcripts, and depth.
Improving accuracy and speeding up Document Image Classification through parallel systems
This paper presents a study showing the benefits of the EfficientNet models compared with heavier Convolutional Neural Networks (CNNs) in the Document Classification task, essential problem in the digitalization process of institutions.
Ranked #1 on
Multi-Modal Document Classification
on Tobacco-3482
Explore, Discover and Learn: Unsupervised Discovery of State-Covering Skills
We perform an extensive evaluation of skill discovery methods on controlled environments and show that EDL offers significant advantages, such as overcoming the coverage problem, reducing the dependence of learned skills on the initial state, and allowing the user to define a prior over which behaviors should be learned.
Budget-aware Semi-Supervised Semantic and Instance Segmentation
Methods that move towards less supervised scenarios are key for image segmentation, as dense labels demand significant human intervention.
Wav2Pix: Speech-conditioned Face Generation using Generative Adversarial Networks
Speech is a rich biometric signal that contains information about the identity, gender and emotional state of the speaker.
The Liver Tumor Segmentation Benchmark (LiTS)
In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018.
Importance Weighted Evolution Strategies
Evolution Strategies (ES) emerged as a scalable alternative to popular Reinforcement Learning (RL) techniques, providing an almost perfect speedup when distributed across hundreds of CPU cores thanks to a reduced communication overhead.
Cross-modal Embeddings for Video and Audio Retrieval
The increasing amount of online videos brings several opportunities for training self-supervised neural networks.
Recurrent Neural Networks for Semantic Instance Segmentation
We present a recurrent model for semantic instance segmentation that sequentially generates binary masks and their associated class probabilities for every object in an image.
Detection-aided liver lesion segmentation using deep learning
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 according treatments.
Skip RNN: Learning to Skip State Updates in Recurrent Neural Networks
We introduce the Skip RNN model which extends existing RNN models by learning to skip state updates and shortens the effective size of the computational graph.
Disentangling Motion, Foreground and Background Features in Videos
This paper introduces an unsupervised framework to extract semantically rich features for video representation.
SalGAN: Visual Saliency Prediction with Generative Adversarial Networks
We introduce SalGAN, a deep convolutional neural network for visual saliency prediction trained with adversarial examples.
Hierarchical Object Detection with Deep Reinforcement Learning
We argue that, while this loss seems unavoidable when working with large amounts of object candidates, the much more reduced amount of region proposals generated by our reinforcement learning agent allows considering to extract features for each location without sharing convolutional computation among regions.
