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Found 17 papers, 3 papers with code
Generating Synthetic Data for Conversational Music Recommendation Using Random Walks and Language Models
Conversational recommendation systems (CRSs) enable users to use natural language feedback to control their recommendations, overcoming many of the challenges of traditional recommendation systems.
IT/IST/IPLeiria Response to the Call for Proposals on JPEG Pleno Point Cloud Coding
This document describes a deep learning-based point cloud geometry codec and a deep learning-based point cloud joint geometry and colour codec, submitted to the Call for Proposals on JPEG Pleno Point Cloud Coding issued in January 2022.
Joint Geometry and Color Projection-based Point Cloud Quality Metric
Moreover, the proposed point cloud quality metric exploits the best performing 2D quality metrics in the literature to assess the quality of the projected images.
Ranked #1 on
Point Cloud Quality Assessment
on M-PCCD
Multi-Perspective LSTM for Joint Visual Representation Learning
We validate the performance of our proposed architecture in the context of two multi-perspective visual recognition tasks namely lip reading and face recognition.
CapsField: Light Field-based Face and Expression Recognition in the Wild using Capsule Routing
A subset of the in the wild dataset contains facial images with different expressions, annotated for usage in the context of face expression recognition tests.
Faithful Embeddings for Knowledge Base Queries
We address this problem with a novel QE method that is more faithful to deductive reasoning, and show that this leads to better performance on complex queries to incomplete KBs.
Long Short-Term Memory with Gate and State Level Fusion for Light Field-Based Face Recognition
In this context, this paper proposes two novel LSTM cell architectures that are able to jointly learn from multiple sequences simultaneously acquired, targeting to create richer and more effective models for recognition tasks.
Face Recognition: A Novel Multi-Level Taxonomy based Survey
In a world where security issues have been gaining growing importance, face recognition systems have attracted increasing attention in multiple application areas, ranging from forensics and surveillance to commerce and entertainment.
A Double-Deep Spatio-Angular Learning Framework for Light Field based Face Recognition
This paper proposes a double-deep spatio-angular learning framework for light field based face recognition, which is able to learn both texture and angular dynamics in sequence using convolutional representations; this is a novel recognition framework that has never been proposed before for either face recognition or any other visual recognition task.
Multinomial Loss on Held-out Data for the Sparse Non-negative Matrix Language Model
In experiments on the one billion words language modeling benchmark, we are able to slightly improve on our previous results which use a different loss function, and employ leave-one-out training on a subset of the main training set.
Controlling Complexity in Part-of-Speech Induction
We consider the problem of fully unsupervised learning of grammatical (part-of-speech) categories from unlabeled text.
Posterior vs Parameter Sparsity in Latent Variable Models
We apply this new method to learn first-order HMMs for unsupervised part-of-speech (POS) tagging, and show that HMMs learned this way consistently and significantly out-performs both EM-trained HMMs, and HMMs with a sparsity-inducing Dirichlet prior trained by variational EM.
Group Sparse Coding
Bag-of-words document representations are often used in text, image and video processing.
Exact Convex Confidence-Weighted Learning
Confidence-weighted (CW) learning [6], an online learning method for linear classifiers, maintains a Gaussian distributions over weight vectors, with a covariance matrix that represents uncertainty about weights and correlations.
Learning Bounds for Domain Adaptation
Empirical risk minimization offers well-known learning guarantees when training and test data come from the same domain.
Biographies, Bollywood, Boom-boxes and Blenders: Domain Adaptation for Sentiment Classification
Automatic sentiment classification has been extensively studied and applied in recent years.
