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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 |
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Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge |
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Miscellaneous |
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2018 |
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Arxiv |
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BraTS; challenge; brain; tumor; segmentation; machine learning; glioma; glioblastoma; radiomics; survival; progression; RECIST |
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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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ADAS; 600.118 |
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Admin @ si @ BRJ2018 |
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3252 |
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Author |
J.R. Serra; A. Martinez; Jordi Vitria; J.B. Subirana |
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Iconic Representation to Image Retrieval. |
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1997 |
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Jornades d'Intel.ligència Artificial: Noves Tendències (JIA'97) |
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DOC;OR;MV |
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BCNPCL @ bcnpcl @ SMV1997 |
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55 |
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Josep Llados; Felipe Lumbreras; V. Chapaprieta; J. Queralt |
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ICAR: Identity Card Automatic Reader. |
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Miscellaneous |
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2001 |
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Sixth International Conference on Document Analysis and Recognition |
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ICDAR 2001 |
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470–474 |
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ADAS;DAG |
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ADAS @ adas @ LLC2001 |
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112 |
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Jordi Gonzalez; Javier Varona; Xavier Roca; Juan J. Villanueva |
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Title |
Human Sequence Evaluation: towards Knowledge-based Scene Interpretations |
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2003 |
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Frontiers in Artificial Intelligence and Applications 100, 168–177 |
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ISE |
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ISE @ ise @ GVR2003d |
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390 |
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Jordi Gonzalez; Javier Varona; Xavier Roca; Juan J. Villanueva |
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Title |
Human Activity Learning and Recognition from Appearance. |
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2001 |
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World Multiconference on Systemics, Cybernetics and Informatics SCI 2001, XIII:463–466 |
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ISE @ ise @ GVR2001 |
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108 |
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Alejandro Cartas; Jordi Luque; Petia Radeva; Carlos Segura; Mariella Dimiccoli |
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Title |
How Much Does Audio Matter to Recognize Egocentric Object Interactions? |
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2019 |
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Arxiv |
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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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MILAB; no menciona |
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Admin @ si @ CLR2019 |
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3383 |
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Author |
Agnes Borras |
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High-Level Clothes Description Based on Colour-Texture Features. |
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2002 |
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Director: J. Llados, Master Thesis. |
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DAG @ dag @ Bor2002 |
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322 |
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V. Chapaprieta; Ernest Valveny |
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Handwritten Digit Recognition Using Point Distribution Models. |
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2001 |
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Proceedings of the IX Spanish Symposium on Pattern Recognition and Image Analysis, 1:49–54. |
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DAG |
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DAG @ dag @ ChV2001 |
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83 |
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Author |
Ayan Banerjee; Sanket Biswas; Josep Llados; Umapada Pal |
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GraphKD: Exploring Knowledge Distillation Towards Document Object Detection with Structured Graph Creation |
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2024 |
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Arxiv |
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Object detection in documents is a key step to automate the structural elements identification process in a digital or scanned document through understanding the hierarchical structure and relationships between different elements. Large and complex models, while achieving high accuracy, can be computationally expensive and memory-intensive, making them impractical for deployment on resource constrained devices. Knowledge distillation allows us to create small and more efficient models that retain much of the performance of their larger counterparts. Here we present a graph-based knowledge distillation framework to correctly identify and localize the document objects in a document image. Here, we design a structured graph with nodes containing proposal-level features and edges representing the relationship between the different proposal regions. Also, to reduce text bias an adaptive node sampling strategy is designed to prune the weight distribution and put more weightage on non-text nodes. We encode the complete graph as a knowledge representation and transfer it from the teacher to the student through the proposed distillation loss by effectively capturing both local and global information concurrently. Extensive experimentation on competitive benchmarks demonstrates that the proposed framework outperforms the current state-of-the-art approaches. The code will be available at: this https URL. |
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DAG |
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Admin @ si @ BBL2024b |
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4023 |
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Author |
Josep Llados; Young-Bin Kwon |
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Graphics Recognition. Recent Advances and Perspectives |
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2004 |
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LNCS 3080, ISBN: 3–540–22478–5 |
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Springer-Verlag |
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DAG |
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DAG @ dag @ LlK2004 |
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515 |
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V. Valev; B. Sankur; Petia Radeva |
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Generalized Non-Reducible Descriptors. |
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1997 |
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Technical Report. |
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MILAB |
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BCNPCL @ bcnpcl @ VSR1997 |
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65 |
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Daniel Marczak; Grzegorz Rypesc; Sebastian Cygert; Tomasz Trzcinski; Bartłomiej Twardowski |
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Generalized Continual Category Discovery |
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2023 |
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arxiv |
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Most of Continual Learning (CL) methods push the limit of supervised learning settings, where an agent is expected to learn new labeled tasks and not forget previous knowledge. However, these settings are not well aligned with real-life scenarios, where a learning agent has access to a vast amount of unlabeled data encompassing both novel (entirely unlabeled) classes and examples from known classes. Drawing inspiration from Generalized Category Discovery (GCD), we introduce a novel framework that relaxes this assumption. Precisely, in any task, we allow for the existence of novel and known classes, and one must use continual version of unsupervised learning methods to discover them. We call this setting Generalized Continual Category Discovery (GCCD). It unifies CL and GCD, bridging the gap between synthetic benchmarks and real-life scenarios. With a series of experiments, we present that existing methods fail to accumulate knowledge from subsequent tasks in which unlabeled samples of novel classes are present. In light of these limitations, we propose a method that incorporates both supervised and unsupervised signals and mitigates the forgetting through the use of centroid adaptation. Our method surpasses strong CL methods adopted for GCD techniques and presents a superior representation learning performance. |
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LAMP |
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Admin @ si @ MRC2023 |
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3985 |
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Marco Cotogni; Fei Yang; Claudio Cusano; Andrew Bagdanov; Joost Van de Weijer |
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Gated Class-Attention with Cascaded Feature Drift Compensation for Exemplar-free Continual Learning of Vision Transformers |
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2022 |
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Arxiv |
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Marco Cotogni, Fei Yang, Claudio Cusano, Andrew D. Bagdanov, Joost van de Weijer |
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We propose a new method for exemplar-free class incremental training of ViTs. The main challenge of exemplar-free continual learning is maintaining plasticity of the learner without causing catastrophic forgetting of previously learned tasks. This is often achieved via exemplar replay which can help recalibrate previous task classifiers to the feature drift which occurs when learning new tasks. Exemplar replay, however, comes at the cost of retaining samples from previous tasks which for many applications may not be possible. To address the problem of continual ViT training, we first propose gated class-attention to minimize the drift in the final ViT transformer block. This mask-based gating is applied to class-attention mechanism of the last transformer block and strongly regulates the weights crucial for previous tasks. Importantly, gated class-attention does not require the task-ID during inference, which distinguishes it from other parameter isolation methods. Secondly, we propose a new method of feature drift compensation that accommodates feature drift in the backbone when learning new tasks. The combination of gated class-attention and cascaded feature drift compensation allows for plasticity towards new tasks while limiting forgetting of previous ones. Extensive experiments performed on CIFAR-100, Tiny-ImageNet and ImageNet100 demonstrate that our exemplar-free method obtains competitive results when compared to rehearsal based ViT methods. |
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LAMP; no proj |
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Admin @ si @ CYC2022 |
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3827 |
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Author |
Robert Benavente; Maria Vanrell |
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Title |
Fuzzy Colour Naming Based on Sigmoid Membership Functions. |
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Miscellaneous |
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2004 |
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CGIV 2004 Second European Conference on Colour in Graphics, Imaging and Vision, 135:139 |
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Aachen (Germany) |
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CIC |
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CAT @ cat @ BeV2004 |
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441 |
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Author |
M. Gonzalez-Audicana; Xavier Otazu; O. Fors; R Garcia; J. Nuñez |
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Title |
Fusion of different spatial and spectral resolution images: development, apllication and comparison of new methods based on wavelets. |
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Miscellaneous |
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2002 |
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Proceedings of the 1st. International Symposium Recent Advances in Quantitative Remote Sensing. |
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no |
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CAT @ cat @ GOF2002 |
Serial |
291 |
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