@conference {180, title = {Cross-Domain Recommendations Using Attention and~Multitask Learning}, booktitle = {Artificial Intelligence Applications and Innovations}, year = {2025}, publisher = {Springer Nature Switzerland}, organization = {Springer Nature Switzerland}, address = {Limassol}, abstract = {In this study, we propose a novel deep learning method for cross-domain recommendations that effectively combines attention mechanisms, autoencoders, and multitask learning. Our approach leverages multiple datasets from diverse domains and incorporates domain-specific encoders, a shared self-attention mechanism, and a multilayer perceptron (MLP) to capture both intra-domain and inter-domain relationships. By jointly modeling these interactions, we improve recommendation accuracy across domains. Experimental results using the MovieLens dataset demonstrate that our proposed cross-domain recommendation system outperforms traditional approaches including matrix factorization, standard MLPs, and self-attention-based baselines.}, isbn = {978-3-031-96228-8}, doi = {https://doi.org/10.1007/978-3-031-96228-8_29}, author = {Papachronopoulos, Gerasimos and Gkillas, Alexandros and Kosmopoulos, Dimitrios}, editor = {Maglogiannis, Ilias and Iliadis, Lazaros and Andreou, Andreas and Papaleonidas, Antonios} } @article {183, title = {Towards more Inclusive Museums Using AI: Guidance in Sign Language}, journal = {ERCIM News}, volume = {141}, year = {2025}, month = {April}, pages = {21{\textendash}22}, author = {Kosmopoulos, Dimitrios and Papadopoulou, Evanthia and Argyros, Antonis} } @conference {167, title = {Procrustes-DTW: Dynamic Time Warping Variant for the Recognition of Sign Language Utterances}, booktitle = {2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW)}, year = {2023}, abstract = {[color=$\#$333333; font-family: {\textquoteright}HelveticaNeue Regular{\textquoteright}, sans-serif; font-size: 18px; font-variant-ligatures: normal; orphans: 2; widows: 2; text-decoration-thickness: initial; text-decoration-style: initial; text-decoration-color: initial]In this paper we present a method for classifying sign language videos using a variation of dynamic time warping with the Procrustes distance, which is a shape similarity measure appropriate for handshape recognition. We initially extract features from the dominant hand. We then classify the signs using a nearest-neighbor scheme which includes a dynamic time warping variant, which we dub Procrustes-DTW and which is suitable for extracting a similarity measure of sequences of handshapes only. To reduce computational costs it is combined with a curvature-based summarization scheme with a reasonable sacrifice in accuracy. We verify experimentally our approach on a custom dataset and we show its merit compared to the conventional dynamic time warping approach.[/color]}, doi = {10.1109/ICASSPW59220.2023.10193012}, author = {Arvanitis, Nikolaos and Sartinas, Evangelos and Kosmopoulos, Dimitrios} } @proceedings {155, title = {Exploitation of Noisy Automatic Data Annotation and Its Application to Hand Posture Classification}, journal = {International Joint Conference on Computer Vision Imaging and Computer Graphics Theory and Applications}, year = {2022}, abstract = {The success of deep learning in recent years relies on the availability of large amounts of accurately annotated training data. In this work, we investigate a technique for utilizing automatically annotated data in classification problems. Using a small number of manually annotated samples, and a large set of data that feature automatically created, noisy labels, our approach trains a Convolutional Neural Network (CNN) in an iterative manner. The automatic annotations are combined with the predictions of the network in order to gradually expand the training set. In order to evaluate the performance of the proposed approach, we apply it to the problem of hand posture recognition from RGB images. We compare the results of training a CNN classifier with and without the use of our technique. Our method yields a significant increase in average classification accuracy, and also decreases the deviation in class accuracies, thus indicating the validity and the usefulness of the proposed approach.}, author = {Georgios Lydakis and Iason Oikonomidis and Dimitrios Kosmopoulos and Antonis Argyros} } @proceedings {153, title = {A 2-D Wrist Motion Based Sign Language Video Summarization}, journal = {British Machine Vision Conference - ORAL PRESENTATION}, year = {2021}, abstract = {In this paper we present a keyframe extraction scheme based on the wrist motion using differential geometry. More specifically, the time (t)-parameterized Frennet-Serret frame for tracking the signer{\textquoteright}s wrist is used and the curvature of the trajectory, is proposed for the identification of the Sign Language (SL) video keyframes. Specifically, a video frame is characterized as keyframe if on that time instance the t-parameterized curvature function attains a maximum value. Finally, in order to properly define the wrist 2-D motion model, a skeleton tracker is used. The proposed scheme is adaptable, i.e., the number of extracted keyframes varies according to the complexity of the signs, while preserving the semantic content. This in turn makes it attractive for applications like video-calling. Its performance in terms of the achieved compression and intelligibility ratios was evaluated on a ground-truth sequence and outperformed its s-parameterized counterpart (s is the arc length); it also outperformed a moment-based SL summarization technique. Furthermore, the proposed scheme was experimentally evaluated on a dataset containing 5500 signs by SL specialists with very promising results. Finally, the proposed keyframe extraction was evaluated against the aforementioned techniques on the same dataset via the use of a GRU neural network on the gloss classification problem; its superior accuracy in identifying the gloss meaning was confirmed.}, author = {Evangelos Sartinas and Emmanouil Psarakis and Klimis Antzakas and Dimitrios Kosmopoulos} } @proceedings {147, title = {A Method for Recovering Near Infrared Information from RGB Measurements with Application in Precision Agriculture}, journal = {European Signal Processing Conference}, year = {2021}, pages = {616-620}, publisher = {IEEE}, edition = {IEEE}, abstract = {[size= 13.008px]In this work we develop a cost-efficient coupled dictionary learning based method for reconstructing multispectral images using only a single RGB commercial camera, without requiring the sensitivity function of the camera sensor. Considering the very high cost, the acquisition time and reduced mobility of multispectral cameras we claim that this is a very attractive option. In contrast to other approaches, the proposed method is not limited only to spectral bands inside the visible spectrum, but it also considers an even more challenging task, that is the reconstruction of spectral bands outside the visible range closer to the near-infrared wavelengths of the spectrum. Extensive experiments with real data demonstrate the effectiveness and applicability of the proposed method in the precision agriculture domain. To this end, we calculate one of the most widely used vegetation indices, the normalized difference vegetation index (NVDI), which may be used for plant health monitoring.[/size]}, author = {A. Gkillas and D. Kosmopoulos and C. Constantinopoulos and D. Ampeliotis and K. Berberidis} } @proceedings {134, title = {Towards a visual Sign Language dataset for home care services}, journal = {15th IEEE International Conference on Face and Gesture Recognition}, volume = {1}, year = {2020}, month = {2020}, pages = {622-626}, abstract = {[size= 13.008px]We present our work towards creating a dataset, which is intended to be used for the implementation of a home care services system for the deaf. The dataset includes recorded realistic scenarios of interactions between deaf patients and mental health experts in their native sign language. The scenarios allow for contextualized representations, in contrast to typical datasets presenting isolated signs or sentences. It includes continuous videos in RGB and depth, which are challenging to analyze and closely resemble real-life scenarios. The research on representation of signs is supported by providing the hand shapes and trajectories for every video using hand and skeleton models, as well as facial features. Furthermore, the dataset may be used for the study of emotional context in Sign Language, since such conversations are typically emotionally charged. [/size]}, author = {D. Kosmopoulos and I. Oikonomidis and K. Konstantinopoulos and N. Arvanitis and K. Antzakas and A. Bifis and G. Lydakis and A. Roussos and A. Argyros} } @proceedings {133, title = {Translation of Sign Language Glosses to Text Using Sequence-to-Sequence Attention Models}, journal = {The 15th International Conference on Signal Image Technology \& Internet based Systems - SITIS}, year = {2019}, month = {2019}, abstract = {This work deals with the problem of Sign Language Translation and more specifically with translating Glosses to text. We applied Sequence to Sequence models with attention mechanism to a parallel gloss to English corpus. This is the first work that used these models to translate American gloss sentences to English. We present our experiments on several network architectures with three different attention functions. The results are very promising and can be useful for the further implementation of a full sign language recognition system.}, author = {N. Arvanitis and C. Constantinopoulos and D. Kosmopoulos} } @article {129, title = {A framework for online segmentation and classification of modeled actions performed in the context of unmodeled ones}, journal = {IEEE Transactions on Circuits and Systems for Video Technology}, volume = {27}, year = {2017}, pages = {2578-2590}, abstract = {In this work, we propose a discriminative framework for online simultaneous segmentation and classification of modeled visual actions that can be performed in the context of other, unknown actions.To this end, we employ Hough transform to vote in a 3D space for the begin point, the end point and the label of the segmented part of the input stream. An SVM is used to model each class and to suggest putative labeled segments on the timeline.To identify the most plausible segments among the putative ones we apply a dynamic programming algorithm, which maximizes the likelihood for label assignment in linear time. The performance of our method is evaluated on synthetic, as well as on real data (Weizmann, TUM Kitchen, UTKAD and Berkeley multimodal human action databases). Extensive quantitative results obtained on a number of standard datasets demonstrate that the proposed approach is of comparable accuracy to the state of the art for online stream segmentation and classification when all performed actions are known and performs considerably better in the presence of unmodeled actions.}, author = {Dimitrios Kosmopoulos and Konstantinos Papoutsakis and Antonis Argyros} } @article {87, title = {Non-invasive analysis of sleep patterns via multimodal sensor input}, journal = {Personal and Ubiquitous Computing}, volume = {18}, year = {2014}, pages = {19-26}, doi = {10.1007/s00779-012-0623-1}, author = {Vangelis Metsis and Dimitrios Kosmopoulos and Vassilis Athitsos and Fillia Makedon} } @proceedings {125, title = {Online segmentation and classification of modeled actions performed in the context of unmodeled ones}, journal = {British Machine Vision Conference}, year = {2014}, abstract = {In this work, we provide a discriminative framework for online simultaneous segmentation and classification of visual actions, which deals effectively with unknown sequences that may interrupt the known sequential patterns. To this end we employ Hough transform to vote in a 3D space for the begin point, the end point and the label of the segmented part of the input stream. An SVM is used to model each class and to suggest putative labeled segments on the timeline. To identify the most plausible segments among the putative ones we apply a dynamic programming algorithm, which maximises an objective function for label assignment in linear time. The performance of our method is evaluated on synthetic as well as on real data (Weizmann and Berkeley multimodal human action database). The proposed approach is of comparable accuracy to the state of the art for online stream segmentation and classification and performs considerably better in the presence of previously unseen actions.}, author = {Dimitrios Kosmopoulos and Kostas Papoutsakis and Antonis Argyros} } @proceedings {90, title = {Developing visual competencies for socially assistive robots: the HOBBIT approach}, journal = {6th International Conference on Pervasive Technologies for Assistive Environments}, year = {2013}, abstract = {In this paper, we present our approach towards developing visual competencies for socially assistive robots within the framework of the HOBBIT project. We show how we integrated several vision modules using a layered architectural scheme. Our goal is to endow the mobile robot with visual perception capabilities so that it can interact with the users. We present the key modules of independent motion detection, object detection, body localization, person tracking, head pose estimation and action recognition and we explain how they serve the goal of natural integration of robots in social environments.}, author = {Kostas Papoutsakis and Pashalis Panteleris and Antonis Ntelidakis and Stavros Stefanou and Xenofon Zabulis and Dimitrios Kosmopoulos and Antonis Argyros} } @conference {86, title = {Hand Shape and 3D Pose Estimation Using Depth Data from a Single Cluttered Frame.}, booktitle = {International Symposium on Visual Computing (ISVC)}, year = {2012}, publisher = {Springer}, organization = {Springer}, keywords = {dblp}, isbn = {978-3-642-33178-7}, author = {Doliotis, Paul and Athitsos, Vassilis and Kosmopoulos, Dimitrios I. and Perantonis, Stavros J.}, editor = {Bebis, George and Boyle, Richard and Parvin, Bahram and Koracin, Darko and Fowlkes, Charless and Wang, Sen and Choi, Min-Hyung and Mantler, Stephan and Schulze, J{\"u}rgen P. and Acevedo, Daniel and Mueller, Klaus and Papka, Michael E.} } @article {21, title = {A Threefold Dataset for Activity and Workflow Recognition in Complex Industrial Environments}, journal = {MultiMedia, IEEE}, volume = {19}, year = {2012}, month = {July-Sept.}, pages = {42 -52}, issn = {1070-986X}, doi = {10.1109/MMUL.2012.31}, author = {Athanasios Voulodimos and D. Kosmopoulos and Vasileiou, Georgios and Sardis, Emmanuel and Anagnostopoulos, Vasileios and Lalos, Constantinos and Doulamis, A.D. and Varvarigou, T.A.} } @proceedings {10, title = {A Dataset for Workflow Recognition in Industrial Scenes}, journal = {IEEE International Conference on Image Processing}, year = {2011}, pages = {3310-3313}, author = {A. Voulodimos and D. Kosmopoulos and Vasileiou, G. and Sardis, E. and Doulamis, A.D. and Anagnostopoulos, V. and Lalos, C. and Varvarigou, T.A.} } @article {28, title = {Detecting abnormal human behaviour using multiple cameras}, journal = {Signal Processing}, volume = {89}, year = {2009}, pages = {1723 - 1738}, keywords = {Behaviour understanding, hidden Markov model, Homography, Support vector machine, Trajectory}, issn = {0165-1684}, doi = {10.1016/j.sigpro.2009.03.016}, author = {Panagiota Antonakaki and Dimitrios Kosmopoulos and Stavros J. Perantonis} } @conference {85, title = {Violence content classification using audio features}, booktitle = {Proceedings of the 4th Helenic conference on Advances in Artificial Intelligence}, year = {2006}, publisher = {Springer-Verlag}, organization = {Springer-Verlag}, address = {Berlin, Heidelberg}, isbn = {3-540-34117-X, 978-3-540-34117-8}, author = {Giannakopoulos, Theodoros and Kosmopoulos, Dimitrios and Aristidou, Andreas and Theodoridis, Sergios} }