output
20162026
most citedTurbo Autoencoder: Deep learning based channel codes for point-to-point communication channels

84 citations

Showing cs.CVShow all

11 papers · 1 filter

cs.CV20242 cited

A Modular System for Enhanced Robustness of Multimedia Understanding Networks via Deep Parametric Estimation

Francesco Barbato, Umberto Michieli, Mehmet Kerim Yucel +2

In multimedia understanding tasks, corrupted samples pose a critical challenge, because when fed to machine learning models they lead to performance degradation. In the past, three…

cs.CV20225 cited

Relevance-based Margin for Contrastively-trained Video Retrieval Models

Alex Falcon, Swathikiran Sudhakaran, Giuseppe Serra +2

Video retrieval using natural language queries has attracted increasing interest due to its relevance in real-world applications, from intelligent access in private media galleries…

cs.CV20229 cited

Fast Hybrid Image Retargeting

Daniel Valdez-Balderas, Oleg Muraveynyk, Timothy Smith

Image retargeting changes the aspect ratio of images while aiming to preserve content and minimise noticeable distortion. Fast and high-quality methods are particularly relevant at…

cs.CV2021

Improving memory banks for unsupervised learning with large mini-batch, consistency and hard negative mining

Adrian Bulat, Enrique Sánchez-Lozano, Georgios Tzimiropoulos

An important component of unsupervised learning by instance-based discrimination is a memory bank for storing a feature representation for each training sample in the dataset. In t…

cs.CV2020

Towards Uncovering the Intrinsic Data Structures for Unsupervised Domain Adaptation using Structurally Regularized Deep Clustering

Hui Tang, Xiatian Zhu, Ke Chen +2

Unsupervised domain adaptation (UDA) is to learn classification models that make predictions for unlabeled data on a target domain, given labeled data on a source domain whose dist…

cs.CV20201 cited

A Transfer Learning approach to Heatmap Regression for Action Unit intensity estimation

Ioanna Ntinou, Enrique Sanchez, Adrian Bulat +2

Action Units (AUs) are geometrically-based atomic facial muscle movements known to produce appearance changes at specific facial locations. Motivated by this observation we propose…