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20162026
most citedSEQ^3: Differentiable Sequence-to-Sequence-to-Sequence Autoencoder for Unsupervised Abstractive Sentence Compression

22 citations · 108 across the 43 of their papers we have counts for

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Showing 2023Show all

6 papers · 1 filter

cs.CL2023

PowMix: A Versatile Regularizer for Multimodal Sentiment Analysis

Efthymios Georgiou, Yannis Avrithis, Alexandros Potamianos

Multimodal sentiment analysis (MSA) leverages heterogeneous data sources to interpret the complex nature of human sentiments. Despite significant progress in multimodal architectur…

cs.SD2023

From West to East: Who can understand the music of the others better?

Charilaos Papaioannou, Emmanouil Benetos, Alexandros Potamianos

Recent developments in MIR have led to several benchmark deep learning models whose embeddings can be used for a variety of downstream tasks. At the same time, the vast majority of…

cs.CV2023★ 1 cited

Self-Attention Based Generative Adversarial Networks For Unsupervised Video Summarization

Maria Nektaria Minaidi, Charilaos Papaioannou, Alexandros Potamianos

In this paper, we study the problem of producing a comprehensive video summary following an unsupervised approach that relies on adversarial learning. We build on a popular method…

cs.CL2023

SeqAug: Sequential Feature Resampling as a modality agnostic augmentation method

Efthymios Georgiou, Alexandros Potamianos

Data augmentation is a prevalent technique for improving performance in various machine learning applications. We propose SeqAug, a modality-agnostic augmentation method that is ta…

cs.SD2023★ 3 cited

Efficient Audio Captioning Transformer with Patchout and Text Guidance

Thodoris Kouzelis, Grigoris Bastas, Athanasios Katsamanis +1

Automated audio captioning is multi-modal translation task that aim to generate textual descriptions for a given audio clip. In this paper we propose a full Transformer architectur…

cs.CL2023★ 3 cited

Depression detection in social media posts using affective and social norm features

Ilias Triantafyllopoulos, Georgios Paraskevopoulos, Alexandros Potamianos

We propose a deep architecture for depression detection from social media posts. The proposed architecture builds upon BERT to extract language representations from social media po…