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| Author | Arnau Baro; Pau Riba; Alicia Fornes | ||||
| Title | Musigraph: Optical Music Recognition Through Object Detection and Graph Neural Network | Type | Conference Article | ||
| Year | 2022 | Publication | Frontiers in Handwriting Recognition. International Conference on Frontiers in Handwriting Recognition (ICFHR2022) | Abbreviated Journal | |
| Volume | 13639 | Issue | Pages | 171-184 | |
| Keywords | Object detection; Optical music recognition; Graph neural network | ||||
| Abstract | During the last decades, the performance of optical music recognition has been increasingly improving. However, and despite the 2-dimensional nature of music notation (e.g. notes have rhythm and pitch), most works treat musical scores as a sequence of symbols in one dimension, which make their recognition still a challenge. Thus, in this work we explore the use of graph neural networks for musical score recognition. First, because graphs are suited for n-dimensional representations, and second, because the combination of graphs with deep learning has shown a great performance in similar applications. Our methodology consists of: First, we will detect each isolated/atomic symbols (those that can not be decomposed in more graphical primitives) and the primitives that form a musical symbol. Then, we will build the graph taking as root node the notehead and as leaves those primitives or symbols that modify the note’s rhythm (stem, beam, flag) or pitch (flat, sharp, natural). Finally, the graph is translated into a human-readable character sequence for a final transcription and evaluation. Our method has been tested on more than five thousand measures, showing promising results. | ||||
| Address | December 04 – 07, 2022; Hyderabad, India | ||||
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| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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| Area | Expedition | Conference | ICFHR | ||
| Notes | DAG; 600.162; 600.140; 602.230 | Approved | no | ||
| Call Number | Admin @ si @ BRF2022b | Serial | 3740 | ||
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| Author | Carlos Boned Riera; Oriol Ramos Terrades | ||||
| Title | Discriminative Neural Variational Model for Unbalanced Classification Tasks in Knowledge Graph | Type | Conference Article | ||
| Year | 2022 | Publication | 26th International Conference on Pattern Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 2186-2191 | ||
| Keywords | Measurement; Couplings; Semantics; Ear; Benchmark testing; Data models; Pattern recognition | ||||
| Abstract | Nowadays the paradigm of link discovery problems has shown significant improvements on Knowledge Graphs. However, method performances are harmed by the unbalanced nature of this classification problem, since many methods are easily biased to not find proper links. In this paper we present a discriminative neural variational auto-encoder model, called DNVAE from now on, in which we have introduced latent variables to serve as embedding vectors. As a result, the learnt generative model approximate better the underlying distribution and, at the same time, it better differentiate the type of relations in the knowledge graph. We have evaluated this approach on benchmark knowledge graph and Census records. Results in this last data set are quite impressive since we reach the highest possible score in the evaluation metrics. However, further experiments are still needed to deeper evaluate the performance of the method in more challenging tasks. | ||||
| Address | Montreal; Quebec; Canada; August 2022 | ||||
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| Area | Expedition | Conference | ICPR | ||
| Notes | DAG; 600.121; 600.162 | Approved | no | ||
| Call Number | Admin @ si @ BoR2022 | Serial | 3741 | ||
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| Author | Patricia Suarez; Angel Sappa | ||||
| Title | A Generative Model for Guided Thermal Image Super-Resolution | Type | Conference Article | ||
| Year | 2024 | Publication | 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications | Abbreviated Journal | |
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| Abstract | This paper presents a novel approach for thermal super-resolution based on a fusion prior, low-resolution thermal image and H brightness channel of the corresponding visible spectrum image. The method combines bicubic interpolation of the ×8 scale target image with the brightness component. To enhance the guidance process, the original RGB image is converted to HSV, and the brightness channel is extracted. Bicubic interpolation is then applied to the low-resolution thermal image, resulting in a Bicubic-Brightness channel blend. This luminance-bicubic fusion is used as an input image to help the training process. With this fused image, the cyclic adversarial generative network obtains high-resolution thermal image results. Experimental evaluations show that the proposed approach significantly improves spatial resolution and pixel intensity levels compared to other state-of-the-art techniques, making it a promising method to obtain high-resolution thermal. | ||||
| Address | Roma; Italia; February 2024 | ||||
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| Area | Expedition | Conference | VISAPP | ||
| Notes | MSIAU | Approved | no | ||
| Call Number | Admin @ si @ SuS2024 | Serial | 4002 | ||
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| Author | Hector Laria Mantecon; Kai Wang; Joost Van de Weijer; Bogdan Raducanu; Kai Wang | ||||
| Title | NeRF-Diffusion for 3D-Consistent Face Generation and Editing | Type | Conference Article | ||
| Year | 2024 | Publication | 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications | Abbreviated Journal | |
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| Abstract | Generating high-fidelity 3D-aware images without 3D supervision is a valuable capability in various applications. Current methods based on NeRF features, SDF information, or triplane features have limited variation after training. To address this, we propose a novel approach that combines pretrained models for shape and content generation. Our method leverages a pretrained Neural Radiance Field as a shape prior and a diffusion model for content generation. By conditioning the diffusion model with 3D features, we enhance its ability to generate novel views with 3D awareness. We introduce a consistency token shared between the NeRF module and the diffusion model to maintain 3D consistency during sampling. Moreover, our framework allows for text editing of 3D-aware image generation, enabling users to modify the style over 3D views while preserving semantic content. Our contributions include incorporating 3D awareness into a text-to-image model, addressing identity consistency in 3D view synthesis, and enabling text editing of 3D-aware image generation. We provide detailed explanations, including the shape prior based on the NeRF model and the content generation process using the diffusion model. We also discuss challenges such as shape consistency and sampling saturation. Experimental results demonstrate the effectiveness and visual quality of our approach. | ||||
| Address | Roma; Italia; February 2024 | ||||
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| Area | Expedition | Conference | VISAPP | ||
| Notes | LAMP | Approved | no | ||
| Call Number | Admin @ si @ LWW2024 | Serial | 4003 | ||
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| Author | Lu Yu; Xialei Liu; Joost Van de Weijer | ||||
| Title | Self-Training for Class-Incremental Semantic Segmentation | Type | Journal Article | ||
| Year | 2022 | Publication | IEEE Transactions on Neural Networks and Learning Systems | Abbreviated Journal | TNNLS |
| Volume | Issue | Pages | |||
| Keywords | Class-incremental learning; Self-training; Semantic segmentation. | ||||
| Abstract | In class-incremental semantic segmentation, we have no access to the labeled data of previous tasks. Therefore, when incrementally learning new classes, deep neural networks suffer from catastrophic forgetting of previously learned knowledge. To address this problem, we propose to apply a self-training approach that leverages unlabeled data, which is used for rehearsal of previous knowledge. Specifically, we first learn a temporary model for the current task, and then, pseudo labels for the unlabeled data are computed by fusing information from the old model of the previous task and the current temporary model. In addition, conflict reduction is proposed to resolve the conflicts of pseudo labels generated from both the old and temporary models. We show that maximizing self-entropy can further improve results by smoothing the overconfident predictions. Interestingly, in the experiments, we show that the auxiliary data can be different from the training data and that even general-purpose, but diverse auxiliary data can lead to large performance gains. The experiments demonstrate the state-of-the-art results: obtaining a relative gain of up to 114% on Pascal-VOC 2012 and 8.5% on the more challenging ADE20K compared to previous state-of-the-art methods. | ||||
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| Notes | LAMP; 600.147; 611.008; | Approved | no | ||
| Call Number | Admin @ si @ YLW2022 | Serial | 3745 | ||
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| Author | Aura Hernandez-Sabate; Jose Elias Yauri; Pau Folch; Daniel Alvarez; Debora Gil | ||||
| Title | EEG Dataset Collection for Mental Workload Predictions in Flight-Deck Environment | Type | Journal Article | ||
| Year | 2024 | Publication | Sensors | Abbreviated Journal | SENS |
| Volume | 24 | Issue | 4 | Pages | 1174 |
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| Abstract | High mental workload reduces human performance and the ability to correctly carry out complex tasks. In particular, aircraft pilots enduring high mental workloads are at high risk of failure, even with catastrophic outcomes. Despite progress, there is still a lack of knowledge about the interrelationship between mental workload and brain functionality, and there is still limited data on flight-deck scenarios. Although recent emerging deep-learning (DL) methods using physiological data have presented new ways to find new physiological markers to detect and assess cognitive states, they demand large amounts of properly annotated datasets to achieve good performance. We present a new dataset of electroencephalogram (EEG) recordings specifically collected for the recognition of different levels of mental workload. The data were recorded from three experiments, where participants were induced to different levels of workload through tasks of increasing cognition demand. The first involved playing the N-back test, which combines memory recall with arithmetical skills. The second was playing Heat-the-Chair, a serious game specifically designed to emphasize and monitor subjects under controlled concurrent tasks. The third was flying in an Airbus320 simulator and solving several critical situations. The design of the dataset has been validated on three different levels: (1) correlation of the theoretical difficulty of each scenario to the self-perceived difficulty and performance of subjects; (2) significant difference in EEG temporal patterns across the theoretical difficulties and (3) usefulness for the training and evaluation of AI models. | ||||
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| Notes | IAM | Approved | no | ||
| Call Number | Admin @ si @ HYF2024 | Serial | 4019 | ||
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| Author | Iban Berganzo-Besga; Hector A. Orengo; Felipe Lumbreras; Paloma Aliende; Monica N. Ramsey | ||||
| Title | Automated detection and classification of multi-cell Phytoliths using Deep Learning-Based Algorithms | Type | Journal Article | ||
| Year | 2022 | Publication | Journal of Archaeological Science | Abbreviated Journal | JArchSci |
| Volume | 148 | Issue | Pages | 105654 | |
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| Abstract | This paper presents an algorithm for automated detection and classification of multi-cell phytoliths, one of the major components of many archaeological and paleoenvironmental deposits. This identification, based on phytolith wave pattern, is made using a pretrained VGG19 deep learning model. This approach has been tested in three key phytolith genera for the study of agricultural origins in Near East archaeology: Avena, Hordeum and Triticum. Also, this classification has been validated at species-level using Triticum boeoticum and dicoccoides images. Due to the diversity of microscopes, cameras and chemical treatments that can influence images of phytolith slides, three types of data augmentation techniques have been implemented: rotation of the images at 45-degree angles, random colour and brightness jittering, and random blur/sharpen. The implemented workflow has resulted in an overall accuracy of 93.68% for phytolith genera, improving previous attempts. The algorithm has also demonstrated its potential to automatize the classification of phytoliths species with an overall accuracy of 100%. The open code and platforms employed to develop the algorithm assure the method's accessibility, reproducibility and reusability. | ||||
| Address | December 2022 | ||||
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| Notes | MSIAU; MACO; 600.167 | Approved | no | ||
| Call Number | Admin @ si @ BOL2022 | Serial | 3753 | ||
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| Author | Shiqi Yang; Yaxing Wang; Kai Wang; Shangling Jui; Joost Van de Weijer | ||||
| Title | Attracting and Dispersing: A Simple Approach for Source-free Domain Adaptation | Type | Conference Article | ||
| Year | 2022 | Publication | 36th Conference on Neural Information Processing Systems | Abbreviated Journal | |
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| Abstract | We propose a simple but effective source-free domain adaptation (SFDA) method.
Treating SFDA as an unsupervised clustering problem and following the intuition that local neighbors in feature space should have more similar predictions than other features, we propose to optimize an objective of prediction consistency. This objective encourages local neighborhood features in feature space to have similar predictions while features farther away in feature space have dissimilar predictions, leading to efficient feature clustering and cluster assignment simultaneously. For efficient training, we seek to optimize an upper-bound of the objective resulting in two simple terms. Furthermore, we relate popular existing methods in domain adaptation, source-free domain adaptation and contrastive learning via the perspective of discriminability and diversity. The experimental results prove the superiority of our method, and our method can be adopted as a simple but strong baseline for future research in SFDA. Our method can be also adapted to source-free open-set and partial-set DA which further shows the generalization ability of our method. |
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| Address | Virtual; November 2022 | ||||
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| Area | Expedition | Conference | NEURIPS | ||
| Notes | LAMP; 600.147 | Approved | no | ||
| Call Number | Admin @ si @ YWW2022a | Serial | 3792 | ||
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| Author | Saiping Zhang; Luis Herranz; Marta Mrak; Marc Gorriz Blanch; Shuai Wan; Fuzheng Yang | ||||
| Title | DCNGAN: A Deformable Convolution-Based GAN with QP Adaptation for Perceptual Quality Enhancement of Compressed Video | Type | Conference Article | ||
| Year | 2022 | Publication | 47th International Conference on Acoustics, Speech, and Signal Processing | Abbreviated Journal | |
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| Abstract | In this paper, we propose a deformable convolution-based generative adversarial network (DCNGAN) for perceptual quality enhancement of compressed videos. DCNGAN is also adaptive to the quantization parameters (QPs). Compared with optical flows, deformable convolutions are more effective and efficient to align frames. Deformable convolutions can operate on multiple frames, thus leveraging more temporal information, which is beneficial for enhancing the perceptual quality of compressed videos. Instead of aligning frames in a pairwise manner, the deformable convolution can process multiple frames simultaneously, which leads to lower computational complexity. Experimental results demonstrate that the proposed DCNGAN outperforms other state-of-the-art compressed video quality enhancement algorithms. | ||||
| Address | Virtual; May 2022 | ||||
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| Area | Expedition | Conference | ICASSP | ||
| Notes | MACO; 600.161; 601.379 | Approved | no | ||
| Call Number | Admin @ si @ ZHM2022a | Serial | 3765 | ||
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| Author | German Barquero; Johnny Nuñez; Sergio Escalera; Zhen Xu; Wei-Wei Tu; Isabelle Guyon | ||||
| Title | Didn’t see that coming: a survey on non-verbal social human behavior forecasting | Type | Conference Article | ||
| Year | 2022 | Publication | Understanding Social Behavior in Dyadic and Small Group Interactions | Abbreviated Journal | |
| Volume | 173 | Issue | Pages | 139-178 | |
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| Abstract | Non-verbal social human behavior forecasting has increasingly attracted the interest of the research community in recent years. Its direct applications to human-robot interaction and socially-aware human motion generation make it a very attractive field. In this survey, we define the behavior forecasting problem for multiple interactive agents in a generic way that aims at unifying the fields of social signals prediction and human motion forecasting, traditionally separated. We hold that both problem formulations refer to the same conceptual problem, and identify many shared fundamental challenges: future stochasticity, context awareness, history exploitation, etc. We also propose a taxonomy that comprises
methods published in the last 5 years in a very informative way and describes the current main concerns of the community with regard to this problem. In order to promote further research on this field, we also provide a summarized and friendly overview of audiovisual datasets featuring non-acted social interactions. Finally, we describe the most common metrics used in this task and their particular issues. |
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| Address | Virtual; June 2022 | ||||
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| Area | Expedition | Conference | PMLR | ||
| Notes | HuPBA; no proj | Approved | no | ||
| Call Number | Admin @ si @ BNE2022 | Serial | 3766 | ||
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| Author | Guillem Martinez; Maya Aghaei; Martin Dijkstra; Bhalaji Nagarajan; Femke Jaarsma; Jaap van de Loosdrecht; Petia Radeva; Klaas Dijkstra | ||||
| Title | Hyper-Spectral Imaging for Overlapping Plastic Flakes Segmentation | Type | Conference Article | ||
| Year | 2022 | Publication | 47th International Conference on Acoustics, Speech, and Signal Processing | Abbreviated Journal | |
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| Keywords | Hyper-spectral imaging; plastic sorting; multi-label segmentation; bitfield encoding | ||||
| Abstract | In this paper, we propose a deformable convolution-based generative adversarial network (DCNGAN) for perceptual quality enhancement of compressed videos. DCNGAN is also adaptive to the quantization parameters (QPs). Compared with optical flows, deformable convolutions are more effective and efficient to align frames. Deformable convolutions can operate on multiple frames, thus leveraging more temporal information, which is beneficial for enhancing the perceptual quality of compressed videos. Instead of aligning frames in a pairwise manner, the deformable convolution can process multiple frames simultaneously, which leads to lower computational complexity. Experimental results demonstrate that the proposed DCNGAN outperforms other state-of-the-art compressed video quality enhancement algorithms. | ||||
| Address | Singapore; May 2022 | ||||
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| Area | Expedition | Conference | ICASSP | ||
| Notes | MILAB; no proj | Approved | no | ||
| Call Number | Admin @ si @ MAD2022 | Serial | 3767 | ||
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| Author | Spencer Low; Oliver Nina; Angel Sappa; Erik Blasch; Nathan Inkawhich | ||||
| Title | Multi-Modal Aerial View Object Classification Challenge Results – PBVS 2022 | Type | Conference Article | ||
| Year | 2022 | Publication | IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) | Abbreviated Journal | |
| Volume | Issue | Pages | 350-358 | ||
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| Abstract | This paper details the results and main findings of the second iteration of the Multi-modal Aerial View Object Classification (MAVOC) challenge. The primary goal of both MAVOC challenges is to inspire research into methods for building recognition models that utilize both synthetic aperture radar (SAR) and electro-optical (EO) imagery. Teams are encouraged to develop multi-modal approaches that incorporate complementary information from both domains. While the 2021 challenge showed a proof of concept that both modalities could be used together, the 2022 challenge focuses on the detailed multi-modal methods. The 2022 challenge uses the same UNIfied Coincident Optical and Radar for recognitioN (UNICORN) dataset and competition format that was used in 2021. Specifically, the challenge focuses on two tasks, (1) SAR classification and (2) SAR + EO classification. The bulk of this document is dedicated to discussing the top performing methods and describing their performance on our blind test set. Notably, all of the top ten teams outperform a Resnet-18 baseline. For SAR classification, the top team showed a 129% improvement over baseline and an 8% average improvement from the 2021 winner. The top team for SAR + EO classification shows a 165% improvement with a 32% average improvement over 2021. | ||||
| Address | New Orleans; USA; June 2022 | ||||
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| Area | Expedition | Conference | CVPRW | ||
| Notes | MSIAU | Approved | no | ||
| Call Number | Admin @ si @ LNS2022 | Serial | 3768 | ||
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| Author | Adam Fodor; Rachid R. Saboundji; Julio C. S. Jacques Junior; Sergio Escalera; David Gallardo Pujol; Andras Lorincz | ||||
| Title | Multimodal Sentiment and Personality Perception Under Speech: A Comparison of Transformer-based Architectures | Type | Conference Article | ||
| Year | 2022 | Publication | Understanding Social Behavior in Dyadic and Small Group Interactions | Abbreviated Journal | |
| Volume | 173 | Issue | Pages | 218-241 | |
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| Abstract | Human-machine, human-robot interaction, and collaboration appear in diverse fields, from homecare to Cyber-Physical Systems. Technological development is fast, whereas real-time methods for social communication analysis that can measure small changes in sentiment and personality states, including visual, acoustic and language modalities are lagging, particularly when the goal is to build robust, appearance invariant, and fair methods. We study and compare methods capable of fusing modalities while satisfying real-time and invariant appearance conditions. We compare state-of-the-art transformer architectures in sentiment estimation and introduce them in the much less explored field of personality perception. We show that the architectures perform differently on automatic sentiment and personality perception, suggesting that each task may be better captured/modeled by a particular method. Our work calls attention to the attractive properties of the linear versions of the transformer architectures. In particular, we show that the best results are achieved by fusing the different architectures{’} preprocessing methods. However, they pose extreme conditions in computation power and energy consumption for real-time computations for quadratic transformers due to their memory requirements. In turn, linear transformers pave the way for quantifying small changes in sentiment estimation and personality perception for real-time social communications for machines and robots. | ||||
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| Area | Expedition | Conference | PMLR | ||
| Notes | HuPBA; no menciona | Approved | no | ||
| Call Number | Admin @ si @ FSJ2022 | Serial | 3769 | ||
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| Author | Emanuele Vivoli; Ali Furkan Biten; Andres Mafla; Dimosthenis Karatzas; Lluis Gomez | ||||
| Title | MUST-VQA: MUltilingual Scene-text VQA | Type | Conference Article | ||
| Year | 2022 | Publication | Proceedings European Conference on Computer Vision Workshops | Abbreviated Journal | |
| Volume | 13804 | Issue | Pages | 345–358 | |
| Keywords | Visual question answering; Scene text; Translation robustness; Multilingual models; Zero-shot transfer; Power of language models | ||||
| Abstract | In this paper, we present a framework for Multilingual Scene Text Visual Question Answering that deals with new languages in a zero-shot fashion. Specifically, we consider the task of Scene Text Visual Question Answering (STVQA) in which the question can be asked in different languages and it is not necessarily aligned to the scene text language. Thus, we first introduce a natural step towards a more generalized version of STVQA: MUST-VQA. Accounting for this, we discuss two evaluation scenarios in the constrained setting, namely IID and zero-shot and we demonstrate that the models can perform on a par on a zero-shot setting. We further provide extensive experimentation and show the effectiveness of adapting multilingual language models into STVQA tasks. | ||||
| Address | Tel-Aviv; Israel; October 2022 | ||||
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| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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| Area | Expedition | Conference | ECCVW | ||
| Notes | DAG; 302.105; 600.155; 611.002 | Approved | no | ||
| Call Number | Admin @ si @ VBM2022 | Serial | 3770 | ||
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| Author | Sergi Garcia Bordils; Andres Mafla; Ali Furkan Biten; Oren Nuriel; Aviad Aberdam; Shai Mazor; Ron Litman; Dimosthenis Karatzas | ||||
| Title | Out-of-Vocabulary Challenge Report | Type | Conference Article | ||
| Year | 2022 | Publication | Proceedings European Conference on Computer Vision Workshops | Abbreviated Journal | |
| Volume | 13804 | Issue | Pages | 359–375 | |
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| Abstract | This paper presents final results of the Out-Of-Vocabulary 2022 (OOV) challenge. The OOV contest introduces an important aspect that is not commonly studied by Optical Character Recognition (OCR) models, namely, the recognition of unseen scene text instances at training time. The competition compiles a collection of public scene text datasets comprising of 326,385 images with 4,864,405 scene text instances, thus covering a wide range of data distributions. A new and independent validation and test set is formed with scene text instances that are out of vocabulary at training time. The competition was structured in two tasks, end-to-end and cropped scene text recognition respectively. A thorough analysis of results from baselines and different participants is presented. Interestingly, current state-of-the-art models show a significant performance gap under the newly studied setting. We conclude that the OOV dataset proposed in this challenge will be an essential area to be explored in order to develop scene text models that achieve more robust and generalized predictions. | ||||
| Address | Tel-Aviv; Israel; October 2022 | ||||
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| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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| Area | Expedition | Conference | ECCVW | ||
| Notes | DAG; 600.155; 302.105; 611.002 | Approved | no | ||
| Call Number | Admin @ si @ GMB2022 | Serial | 3771 | ||
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