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most citedAutomatic Image Content Extraction: Operationalizing Machine Learning in Humanistic Photographic Studies of Large Visual Archives

4 citations · 19 across the 26 of their papers we have counts for

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cs.CV2024

Continual Low-Rank Scaled Dot-product Attention

Ginés Carreto Picón, Illia Oleksiienko, Lukas Hedegaard +2

Transformers are widely used for their ability to capture data relations in sequence processing, with great success for a wide range of static tasks. However, the computational and…

cs.CV2023

Multi-Class Anomaly Detection based on Regularized Discriminative Coupled hypersphere-based Feature Adaptation

Mehdi Rafiei, Alexandros Iosifidis

In anomaly detection, identification of anomalies across diverse product categories is a complex task. This paper introduces a new model by including class discriminative propertie…

cs.CV20232 cited

On Pixel-level Performance Assessment in Anomaly Detection

Mehdi Rafiei, Toby P. Breckon, Alexandros Iosifidis

Anomaly detection methods have demonstrated remarkable success across various applications. However, assessing their performance, particularly at the pixel-level, presents a comple…

cs.CV20221 cited

Semantic Communication Enabling Robust Edge Intelligence for Time-Critical IoT Applications

Andrea Cavagna, Nan Li, Alexandros Iosifidis +1

This paper aims to design robust Edge Intelligence using semantic communication for time-critical IoT applications. We systematically analyze the effect of image DCT coefficients o…

cs.CV20222 cited

Design and Prototyping Distributed CNN Inference Acceleration in Edge Computing

Zhongtian Dong, Nan Li, Alexandros Iosifidis +1

For time-critical IoT applications using deep learning, inference acceleration through distributed computing is a promising approach to meet a stringent deadline. In this paper, we…

cs.CV20224 cited

Automatic Image Content Extraction: Operationalizing Machine Learning in Humanistic Photographic Studies of Large Visual Archives

Anssi Männistö, Mert Seker, Alexandros Iosifidis +1

Applying machine learning tools to digitized image archives has a potential to revolutionize quantitative research of visual studies in humanities and social sciences. The ability…