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Author | Ruben Ballester; Carles Casacuberta; Sergio Escalera | ||||
Title | Decorrelating neurons using persistence | Type | Miscellaneous | ||
Year | 2023 | Publication | ARXIV | Abbreviated Journal | |
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Abstract | We propose a novel way to improve the generalisation capacity of deep learning models by reducing high correlations between neurons. For this, we present two regularisation terms computed from the weights of a minimum spanning tree of the clique whose vertices are the neurons of a given network (or a sample of those), where weights on edges are correlation dissimilarities. We provide an extensive set of experiments to validate the effectiveness of our terms, showing that they outperform popular ones. Also, we demonstrate that naive minimisation of all correlations between neurons obtains lower accuracies than our regularisation terms, suggesting that redundancies play a significant role in artificial neural networks, as evidenced by some studies in neuroscience for real networks. We include a proof of differentiability of our regularisers, thus developing the first effective topological persistence-based regularisation terms that consider the whole set of neurons and that can be applied to a feedforward architecture in any deep learning task such as classification, data generation, or regression. | ||||
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Notes | HUPBA | Approved | no | ||
Call Number | Admin @ si @ BCE2023 | Serial | 3977 | ||
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Author | German Barquero; Sergio Escalera; Cristina Palmero | ||||
Title | Seamless Human Motion Composition with Blended Positional Encodings | Type | Miscellaneous | ||
Year | 2024 | Publication | Arxiv | Abbreviated Journal | |
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Abstract | Conditional human motion generation is an important topic with many applications in virtual reality, gaming, and robotics. While prior works have focused on generating motion guided by text, music, or scenes, these typically result in isolated motions confined to short durations. Instead, we address the generation of long, continuous sequences guided by a series of varying textual descriptions. In this context, we introduce FlowMDM, the first diffusion-based model that generates seamless Human Motion Compositions (HMC) without any postprocessing or redundant denoising steps. For this, we introduce the Blended Positional Encodings, a technique that leverages both absolute and relative positional encodings in the denoising chain. More specifically, global motion coherence is recovered at the absolute stage, whereas smooth and realistic transitions are built at the relative stage. As a result, we achieve state-of-the-art results in terms of accuracy, realism, and smoothness on the Babel and HumanML3D datasets. FlowMDM excels when trained with only a single description per motion sequence thanks to its Pose-Centric Cross-ATtention, which makes it robust against varying text descriptions at inference time. Finally, to address the limitations of existing HMC metrics, we propose two new metrics: the Peak Jerk and the Area Under the Jerk, to detect abrupt transitions. | ||||
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Notes | HUPBA | Approved | no | ||
Call Number | Admin @ si @ BEP2024 | Serial | 4022 | ||
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Author | Xavier Baro; David Masip; Elena Planas; Julia Minguillon | ||||
Title | PeLP: Plataforma para el Aprendizaje de Lenguajes de Programación | Type | Miscellaneous | ||
Year | 2013 | Publication | XV Jornadas de Enseñanza Universitaria de la Informatica | Abbreviated Journal | |
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Area | Expedition | Conference | JENUI | ||
Notes | OR;HuPBA;MV | Approved | no | ||
Call Number | Admin @ si @ BMP2013 | Serial | 2237 | ||
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Author | Spyridon Bakas; Mauricio Reyes; Andras Jakab; Stefan Bauer; Markus Rempfler; Alessandro Crimi; Russell Takeshi Shinohara; Christoph Berger; Sung Min Ha; Martin Rozycki; Marcel Prastawa; Esther Alberts; Jana Lipkova; John Freymann; Justin Kirby; Michel Bilello; Hassan Fathallah-Shaykh; Roland Wiest; Jan Kirschke; Benedikt Wiestler; Rivka Colen; Aikaterini Kotrotsou; Pamela Lamontagne; Daniel Marcus; Mikhail Milchenko; Arash Nazeri; Marc-Andre Weber; Abhishek Mahajan; Ujjwal Baid; Dongjin Kwon; Manu Agarwal; Mahbubul Alam; Alberto Albiol; Antonio Albiol; Varghese Alex; Tuan Anh Tran; Tal Arbel; Aaron Avery; Subhashis Banerjee; Thomas Batchelder; Kayhan Batmanghelich; Enzo Battistella; Martin Bendszus; Eze Benson; Jose Bernal; George Biros; Mariano Cabezas; Siddhartha Chandra; Yi-Ju Chang; Joseph Chazalon; Shengcong Chen; Wei Chen; Jefferson Chen; Kun Cheng; Meinel Christoph; Roger Chylla; Albert Clérigues; Anthony Costa; Xiaomeng Cui; Zhenzhen Dai; Lutao Dai; Eric Deutsch; Changxing Ding; Chao Dong; Wojciech Dudzik; Theo Estienne; Hyung Eun Shin; Richard Everson; Jonathan Fabrizio; Longwei Fang; Xue Feng; Lucas Fidon; Naomi Fridman; Huan Fu; David Fuentes; David G Gering; Yaozong Gao; Evan Gates; Amir Gholami; Mingming Gong; Sandra Gonzalez-Villa; J Gregory Pauloski; Yuanfang Guan; Sheng Guo; Sudeep Gupta; Meenakshi H Thakur; Klaus H Maier-Hein; Woo-Sup Han; Huiguang He; Aura Hernandez-Sabate; Evelyn Herrmann; Naveen Himthani; Winston Hsu; Cheyu Hsu; Xiaojun Hu; Xiaobin Hu; Yan Hu; Yifan Hu; Rui Hua | ||||
Title | Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge | Type | Miscellaneous | ||
Year | 2018 | Publication | Arxiv | Abbreviated Journal | |
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Keywords | BraTS; challenge; brain; tumor; segmentation; machine learning; glioma; glioblastoma; radiomics; survival; progression; RECIST | ||||
Abstract | Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multiparametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e. 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in preoperative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that undergone gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset. | ||||
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Notes | ADAS; 600.118 | Approved | no | ||
Call Number | Admin @ si @ BRJ2018 | Serial | 3252 | ||
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Author | Pierluigi Casale | ||||
Title | Social Environment Description from Data Collected with a Wearable Device | Type | Miscellaneous | ||
Year | 2008 | Publication | CVC Technical Report #124 | Abbreviated Journal | |
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Address | Barcelona, Spain | ||||
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Notes | Approved | no | |||
Call Number | Admin @ si @ Cas2008 | Serial | 1151 | ||
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Author | Pau Cano; Alvaro Caravaca; Debora Gil; Eva Musulen | ||||
Title | Diagnosis of Helicobacter pylori using AutoEncoders for the Detection of Anomalous Staining Patterns in Immunohistochemistry Images | Type | Miscellaneous | ||
Year | 2023 | Publication | Arxiv | Abbreviated Journal | |
Volume | Issue | Pages | 107241 | ||
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Abstract | This work addresses the detection of Helicobacter pylori a bacterium classified since 1994 as class 1 carcinogen to humans. By its highest specificity and sensitivity, the preferred diagnosis technique is the analysis of histological images with immunohistochemical staining, a process in which certain stained antibodies bind to antigens of the biological element of interest. This analysis is a time demanding task, which is currently done by an expert pathologist that visually inspects the digitized samples.
We propose to use autoencoders to learn latent patterns of healthy tissue and detect H. pylori as an anomaly in image staining. Unlike existing classification approaches, an autoencoder is able to learn patterns in an unsupervised manner (without the need of image annotations) with high performance. In particular, our model has an overall 91% of accuracy with 86\% sensitivity, 96% specificity and 0.97 AUC in the detection of H. pylori. |
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Notes | IAM | Approved | no | ||
Call Number | Admin @ si @ CCG2023 | Serial | 3855 | ||
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Author | Antonio Carta; Andrea Cossu; Vincenzo Lomonaco; Davide Bacciu; Joost Van de Weijer | ||||
Title | Projected Latent Distillation for Data-Agnostic Consolidation in Distributed Continual Learning | Type | Miscellaneous | ||
Year | 2023 | Publication | Arxiv | Abbreviated Journal | |
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Abstract | Distributed learning on the edge often comprises self-centered devices (SCD) which learn local tasks independently and are unwilling to contribute to the performance of other SDCs. How do we achieve forward transfer at zero cost for the single SCDs? We formalize this problem as a Distributed Continual Learning scenario, where SCD adapt to local tasks and a CL model consolidates the knowledge from the resulting stream of models without looking at the SCD's private data. Unfortunately, current CL methods are not directly applicable to this scenario. We propose Data-Agnostic Consolidation (DAC), a novel double knowledge distillation method that consolidates the stream of SC models without using the original data. DAC performs distillation in the latent space via a novel Projected Latent Distillation loss. Experimental results show that DAC enables forward transfer between SCDs and reaches state-of-the-art accuracy on Split CIFAR100, CORe50 and Split TinyImageNet, both in reharsal-free and distributed CL scenarios. Somewhat surprisingly, even a single out-of-distribution image is sufficient as the only source of data during consolidation. | ||||
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Notes | LAMP | Approved | no | ||
Call Number | Admin @ si @ CCL2023 | Serial | 3871 | ||
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Author | Anthony Cioppa; Silvio Giancola; Vladimir Somers; Floriane Magera; Xin Zhou; Hassan Mkhallati; Adrien Deliège; Jan Held; Carlos Hinojosa; Amir M. Mansourian; Pierre Miralles; Olivier Barnich; Christophe De Vleeschouwer; Alexandre Alahi; Bernard Ghanem; Marc Van Droogenbroeck; Abdullah Kamal; Adrien Maglo; Albert Clapes; Amr Abdelaziz; Artur Xarles; Astrid Orcesi; Atom Scott; Bin Liu; Byoungkwon Lim; Chen Chen; Fabian Deuser; Feng Yan; Fufu Yu; Gal Shitrit; Guanshuo Wang; Gyusik Choi; Hankyul Kim; Hao Guo; Hasby Fahrudin; Hidenari Koguchi; Håkan Ardo; Ibrahim Salah; Ido Yerushalmy; Iftikar Muhammad; Ikuma Uchida; Ishay Beery; Jaonary Rabarisoa; Jeongae Lee; Jiajun Fu; Jianqin Yin; Jinghang Xu; Jongho Nang; Julien Denize; Junjie Li; Junpei Zhang; Juntae Kim; Kamil Synowiec; Kenji Kobayashi; Kexin Zhang; Konrad Habel; Kota Nakajima; Licheng Jiao; Lin Ma; Lizhi Wang; Luping Wang; Menglong Li; Mengying Zhou; Mohamed Nasr; Mohamed Abdelwahed; Mykola Liashuha; Nikolay Falaleev; Norbert Oswald; Qiong Jia; Quoc-Cuong Pham; Ran Song; Romain Herault; Rui Peng; Ruilong Chen; Ruixuan Liu; Ruslan Baikulov; Ryuto Fukushima; Sergio Escalera; Seungcheon Lee; Shimin Chen; Shouhong Ding; Taiga Someya; Thomas B. Moeslund; Tianjiao Li; Wei Shen; Wei Zhang; Wei Li; Wei Dai; Weixin Luo; Wending Zhao; Wenjie Zhang; Xinquan Yang; Yanbiao Ma; Yeeun Joo; Yingsen Zeng; Yiyang Gan; Yongqiang Zhu; Yujie Zhong; Zheng Ruan; Zhiheng Li; Zhijian Huang; Ziyu Meng | ||||
Title | SoccerNet 2023 Challenges Results | Type | Miscellaneous | ||
Year | 2023 | Publication | Arxiv | Abbreviated Journal | |
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Abstract | The SoccerNet 2023 challenges were the third annual video understanding challenges organized by the SoccerNet team. For this third edition, the challenges were composed of seven vision-based tasks split into three main themes. The first theme, broadcast video understanding, is composed of three high-level tasks related to describing events occurring in the video broadcasts: (1) action spotting, focusing on retrieving all timestamps related to global actions in soccer, (2) ball action spotting, focusing on retrieving all timestamps related to the soccer ball change of state, and (3) dense video captioning, focusing on describing the broadcast with natural language and anchored timestamps. The second theme, field understanding, relates to the single task of (4) camera calibration, focusing on retrieving the intrinsic and extrinsic camera parameters from images. The third and last theme, player understanding, is composed of three low-level tasks related to extracting information about the players: (5) re-identification, focusing on retrieving the same players across multiple views, (6) multiple object tracking, focusing on tracking players and the ball through unedited video streams, and (7) jersey number recognition, focusing on recognizing the jersey number of players from tracklets. Compared to the previous editions of the SoccerNet challenges, tasks (2-3-7) are novel, including new annotations and data, task (4) was enhanced with more data and annotations, and task (6) now focuses on end-to-end approaches. More information on the tasks, challenges, and leaderboards are available on this https URL. Baselines and development kits can be found on this https URL. | ||||
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Notes | HUPBA | Approved | no | ||
Call Number | Admin @ si @ CGS2023 | Serial | 3991 | ||
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Author | Bhaskar Chakraborty | ||||
Title | View-Invariant Human-Body Detection with Extension to Human Action Recognition using Component Wise HMM of Body Parts | Type | Miscellaneous | ||
Year | 2008 | Publication | CVC Technical Report #123 | Abbreviated Journal | |
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Address | Barcelona, Spain | ||||
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Notes | ISE | Approved | no | ||
Call Number | Admin @ si @ Cha2008 | Serial | 1149 | ||
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Author | Francesco Ciompi | ||||
Title | ECOC-based Plaque Classification using In-vivo and Exvivo Intravascular Ultrasound Data | Type | Miscellaneous | ||
Year | 2008 | Publication | CVC Technical Report #125 | Abbreviated Journal | |
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Address | Bellaterra (Spain) | ||||
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Notes | Approved | no | |||
Call Number | Admin @ si @ Cio2008 | Serial | 1145 | ||
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Author | Alejandro Cartas; Jordi Luque; Petia Radeva; Carlos Segura; Mariella Dimiccoli | ||||
Title | How Much Does Audio Matter to Recognize Egocentric Object Interactions? | Type | Miscellaneous | ||
Year | 2019 | Publication | Arxiv | Abbreviated Journal | |
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Abstract | CoRR abs/1906.00634
Sounds are an important source of information on our daily interactions with objects. For instance, a significant amount of people can discern the temperature of water that it is being poured just by using the sense of hearing. However, only a few works have explored the use of audio for the classification of object interactions in conjunction with vision or as single modality. In this preliminary work, we propose an audio model for egocentric action recognition and explore its usefulness on the parts of the problem (noun, verb, and action classification). Our model achieves a competitive result in terms of verb classification (34.26% accuracy) on a standard benchmark with respect to vision-based state of the art systems, using a comparatively lighter architecture. |
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Notes | MILAB; no menciona | Approved | no | ||
Call Number | Admin @ si @ CLR2019 | Serial | 3383 | ||
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Author | Francisco Cruz; Oriol Ramos Terrades | ||||
Title | A probabilistic framework for handwritten text line segmentation | Type | Miscellaneous | ||
Year | 2018 | Publication | Arxiv | Abbreviated Journal | |
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Keywords | Document Analysis; Text Line Segmentation; EM algorithm; Probabilistic Graphical Models; Parameter Learning | ||||
Abstract | We successfully combine Expectation-Maximization algorithm and variational
approaches for parameter learning and computing inference on Markov random fields. This is a general method that can be applied to many computer vision tasks. In this paper, we apply it to handwritten text line segmentation. We conduct several experiments that demonstrate that our method deal with common issues of this task, such as complex document layout or non-latin scripts. The obtained results prove that our method achieve state-of-theart performance on different benchmark datasets without any particular fine tuning step. |
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Notes | DAG; 600.097; 600.121 | Approved | no | ||
Call Number | Admin @ si @ CrR2018 | Serial | 3253 | ||
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Author | Guillem Cucurull; Pau Rodriguez; Vacit Oguz Yazici; Josep M. Gonfaus; Xavier Roca; Jordi Gonzalez | ||||
Title | Deep Inference of Personality Traits by Integrating Image and Word Use in Social Networks | Type | Miscellaneous | ||
Year | 2018 | Publication | Arxiv | Abbreviated Journal | |
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Abstract | arXiv:1802.06757
Social media, as a major platform for communication and information exchange, is a rich repository of the opinions and sentiments of 2.3 billion users about a vast spectrum of topics. To sense the whys of certain social user’s demands and cultural-driven interests, however, the knowledge embedded in the 1.8 billion pictures which are uploaded daily in public profiles has just started to be exploited since this process has been typically been text-based. Following this trend on visual-based social analysis, we present a novel methodology based on Deep Learning to build a combined image-and-text based personality trait model, trained with images posted together with words found highly correlated to specific personality traits. So the key contribution here is to explore whether OCEAN personality trait modeling can be addressed based on images, here called MindPics, appearing with certain tags with psychological insights. We found that there is a correlation between those posted images and their accompanying texts, which can be successfully modeled using deep neural networks for personality estimation. The experimental results are consistent with previous cyber-psychology results based on texts or images. In addition, classification results on some traits show that some patterns emerge in the set of images corresponding to a specific text, in essence to those representing an abstract concept. These results open new avenues of research for further refining the proposed personality model under the supervision of psychology experts. |
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Notes | ISE; 600.098; 600.119 | Approved | no | ||
Call Number | Admin @ si @ CRY2018 | Serial | 3550 | ||
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Author | Soumick Chatterjee; Fatima Saad; Chompunuch Sarasaen; Suhita Ghosh; Rupali Khatun; Petia Radeva; Georg Rose; Sebastian Stober; Oliver Speck; Andreas Nürnberger | ||||
Title | Exploration of Interpretability Techniques for Deep COVID-19 Classification using Chest X-ray Images | Type | Miscellaneous | ||
Year | 2020 | Publication | Arxiv | Abbreviated Journal | |
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Abstract | CoRR abs/2006.02570
The outbreak of COVID-19 has shocked the entire world with its fairly rapid spread and has challenged different sectors. One of the most effective ways to limit its spread is the early and accurate diagnosis of infected patients. Medical imaging such as X-ray and Computed Tomography (CT) combined with the potential of Artificial Intelligence (AI) plays an essential role in supporting the medical staff in the diagnosis process. Thereby, the use of five different deep learning models (ResNet18, ResNet34, InceptionV3, InceptionResNetV2, and DenseNet161) and their Ensemble have been used in this paper, to classify COVID-19, pneumoniæ and healthy subjects using Chest X-Ray. Multi-label classification was performed to predict multiple pathologies for each patient, if present. Foremost, the interpretability of each of the networks was thoroughly studied using techniques like occlusion, saliency, input X gradient, guided backpropagation, integrated gradients, and DeepLIFT. The mean Micro-F1 score of the models for COVID-19 classifications ranges from 0.66 to 0.875, and is 0.89 for the Ensemble of the network models. The qualitative results depicted the ResNets to be the most interpretable model. |
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Notes | MILAB | Approved | no | ||
Call Number | Admin @ si @ CSS2020 | Serial | 3534 | ||
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Author | Alejandro Cartas; Estefania Talavera; Petia Radeva; Mariella Dimiccoli | ||||
Title | On the Role of Event Boundaries in Egocentric Activity Recognition from Photostreams | Type | Miscellaneous | ||
Year | 2018 | Publication | Arxiv | Abbreviated Journal | |
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Abstract | Event boundaries play a crucial role as a pre-processing step for detection, localization, and recognition tasks of human activities in videos. Typically, although their intrinsic subjectiveness, temporal bounds are provided manually as input for training action recognition algorithms. However, their role for activity recognition in the domain of egocentric photostreams has been so far neglected. In this paper, we provide insights of how automatically computed boundaries can impact activity recognition results in the emerging domain of egocentric photostreams. Furthermore, we collected a new annotated dataset acquired by 15 people by a wearable photo-camera and we used it to show the generalization capabilities of several deep learning based architectures to unseen users. | ||||
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Notes | MILAB; no proj | Approved | no | ||
Call Number | Admin @ si @ CTR2018 | Serial | 3184 | ||
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