Search Results for author:
Found 21 papers, 15 papers with code
HyperFast: Instant Classification for Tabular Data
Training deep learning models and performing hyperparameter tuning can be computationally demanding and time-consuming.
Sign Language Translation from Instructional Videos
We report a result of 8. 03 on the BLEU score, and publish the first open-source implementation of its kind to promote further advances.
Ranked #1 on
Sign Language Translation
on How2Sign
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
FocusNews (test)
on WMT-SLT
Model Zoos: A Dataset of Diverse Populations of Neural Network Models
With this work, we publish a novel dataset of model zoos containing systematically generated and diverse populations of NN models for further research.
Hyper-Representations as Generative Models: Sampling Unseen Neural Network Weights
Learning representations of neural network weights given a model zoo is an emerging and challenging area with many potential applications from model inspection, to neural architecture search or knowledge distillation.
Hyper-Representations for Pre-Training and Transfer Learning
Learning representations of neural network weights given a model zoo is an emerging and challenging area with many potential applications from model inspection, to neural architecture search or knowledge distillation.
Multiple Object Tracking with Mixture Density Networks for Trajectory Estimation
In this work, we show that trajectory estimation can become a key factor for tracking, and present TrajE, a trajectory estimator based on recurrent mixture density networks, as a generic module that can be added to existing object trackers.
Ranked #25 on
Multi-Object Tracking
on MOT17
(MOTA metric)
Curriculum Learning for Recurrent Video Object Segmentation
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 networks.
Assessing Knee OA Severity with CNN attention-based end-to-end architectures
This work proposes a novel end-to-end convolutional neural network (CNN) architecture to automatically quantify the severity of knee osteoarthritis (OA) using X-Ray images, which incorporates trainable attention modules acting as unsupervised fine-grained detectors of the region of interest (ROI).
Hyperparameter-Free Losses for Model-Based Monocular Reconstruction
This work proposes novel hyperparameter-free losses for single view 3D reconstruction with morphable models (3DMM).
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.
Cross-modal Embeddings for Video and Audio Retrieval
The increasing amount of online videos brings several opportunities for training self-supervised neural networks.
Semantic Summarization of Egocentric Photo Stream Events
With the rapid increase of users of wearable cameras in recent years and of the amount of data they produce, there is a strong need for automatic retrieval and summarization techniques.
Diving Deep into Sentiment: Understanding Fine-tuned CNNs for Visual Sentiment Prediction
Visual media are powerful means of expressing emotions and sentiments.
End-to-end Convolutional Network for Saliency Prediction
The prediction of saliency areas in images has been traditionally addressed with hand crafted features based on neuroscience principles.
Improving Spatial Codification in Semantic Segmentation
This paper explores novel approaches for improving the spatial codification for the pooling of local descriptors to solve the semantic segmentation problem.
Visual Summary of Egocentric Photostreams by Representative Keyframes
Building a visual summary from an egocentric photostream captured by a lifelogging wearable camera is of high interest for different applications (e. g. memory reinforcement).
Quality Control in Crowdsourced Object Segmentation
This paper explores processing techniques to deal with noisy data in crowdsourced object segmentation tasks.
Visual Information Retrieval in Endoscopic Video Archives
In endoscopic procedures, surgeons work with live video streams from the inside of their subjects.
Exploring EEG for Object Detection and Retrieval
We show that it is indeed possible to detect such objects in complex images and, also, that users with previous knowledge on the dataset or experience with the RSVP outperform others.
