output
20042026
most citedA Comprehensive Study on Deep Learning-based Methods for Sign Language Recognition

232 citations

Showing 2022Show all

9 papers · 1 filter

cs.CV2022

Cross-Stitched Multi-task Dual Recursive Networks for Unified Single Image Deraining and Desnowing

Sotiris Karavarsamis, Alexandros Doumanoglou, Konstantinos Konstantoudakis +1

We present the Cross-stitched Multi-task Unified Dual Recursive Network (CMUDRN) model targeting the task of unified deraining and desnowing in a multi-task learning setting. This…

cs.CY2022★ 92 cited

FAIR for AI: An interdisciplinary and international community building perspective

E. A. Huerta, Ben Blaiszik, L. Catherine Brinson +21

A foundational set of findable, accessible, interoperable, and reusable (FAIR) principles were proposed in 2016 as prerequisites for proper data management and stewardship, with th…

eess.AS2022★ 31 cited

Open Challenges in Synthetic Speech Detection

Luca Cuccovillo, Christoforos Papastergiopoulos, Anastasios Vafeiadis +4

In this paper the current status and open challenges of synthetic speech detection are addressed. The work comprises an initial analysis of available open datasets and of existing…

cs.CV2022★ 19 cited

Hybrid Skip: A Biologically Inspired Skip Connection for the UNet Architecture

Nikolaos Zioulis, Georgios Albanis, Petros Drakoulis +3

In this work we introduce a biologically inspired long-range skip connection for the UNet architecture that relies on the perceptual illusion of hybrid images, being images that si…

cs.RO2022★ 106 cited

A Survey of Robotic Harvesting Systems and Enabling Technologies

Leonidas Droukas, Zoe Doulgeri, Nikolaos L. Tsakiridis +7

This paper presents a comprehensive review of ground agricultural robotic systems and applications with special focus on harvesting that span research and commercial products and r…

cs.CV2022★ 15 cited

ViGAT: Bottom-up event recognition and explanation in video using factorized graph attention network

Nikolaos Gkalelis, Dimitrios Daskalakis, Vasileios Mezaris

In this paper a pure-attention bottom-up approach, called ViGAT, that utilizes an object detector together with a Vision Transformer (ViT) backbone network to derive object and fra…