| Records |
Author  |
B. Moghaddam; David Guillamet; Jordi Vitria |
| Title |
, Local Appearance-Based Models using High-Order Statistics of Image Features |
Type |
Miscellaneous |
| Year |
2003 |
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IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) |
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OR;MV |
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no |
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BCNPCL @ bcnpcl @ MGV2003 |
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395 |
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| Author |
B. Moghaddam; David Guillamet; Jordi Vitria |
| Title |
Local Appearance-Based Models using High-Order Statistics of Image Features |
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Miscellaneous |
| Year |
2003 |
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Mitsubishi Electrical Reasearch Lab Technical Report |
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OR;MV |
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no |
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BCNPCL @ bcnpcl @ TR2003-85 |
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396 |
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| Author |
B. Zhou; Agata Lapedriza; J. Xiao; A. Torralba; A. Oliva |
| Title |
Learning Deep Features for Scene Recognition using Places Database |
Type |
Conference Article |
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2014 |
Publication |
28th Annual Conference on Neural Information Processing Systems |
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487-495 |
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Montreal; Canada; December 2014 |
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NIPS |
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OR;MV |
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no |
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Admin @ si @ ZLX2014 |
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2621 |
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| Author |
Baiyu Chen; Sergio Escalera; Isabelle Guyon; Victor Ponce; N. Shah; Marc Oliu |
| Title |
Overcoming Calibration Problems in Pattern Labeling with Pairwise Ratings: Application to Personality Traits |
Type |
Conference Article |
| Year |
2016 |
Publication |
14th European Conference on Computer Vision Workshops |
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Calibration of labels; Label bias; Ordinal labeling; Variance Models; Bradley-Terry-Luce model; Continuous labels; Regression; Personality traits; Crowd-sourced labels |
| Abstract |
We address the problem of calibration of workers whose task is to label patterns with continuous variables, which arises for instance in labeling images of videos of humans with continuous traits. Worker bias is particularly dicult to evaluate and correct when many workers contribute just a few labels, a situation arising typically when labeling is crowd-sourced. In the scenario of labeling short videos of people facing a camera with personality traits, we evaluate the feasibility of the pairwise ranking method to alleviate bias problems. Workers are exposed to pairs of videos at a time and must order by preference. The variable levels are reconstructed by fitting a Bradley-Terry-Luce model with maximum likelihood. This method may at first sight, seem prohibitively expensive because for N videos, p = N (N-1)/2 pairs must be potentially processed by workers rather that N videos. However, by performing extensive simulations, we determine an empirical law for the scaling of the number of pairs needed as a function of the number of videos in order to achieve a given accuracy of score reconstruction and show that the pairwise method is a ordable. We apply the method to the labeling of a large scale dataset of 10,000 videos used in the ChaLearn Apparent Personality Trait challenge. |
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Amsterdam; The Netherlands; October 2016 |
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ECCVW |
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HuPBA;MILAB; |
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no |
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Admin @ si @ CEG2016 |
Serial |
2829 |
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| Author |
Bart M. Ter Haar Romeny; W. Niessen; J. Weickert; P. Van Roermund; W. Van Enk; Antonio Lopez; R. Maas |
| Title |
Orientation detection of trabecular bone |
Type |
Miscellaneous |
| Year |
1996 |
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Biophysics and Molecular Biology, International Biophysics Congress. Volume 65, pgs. P–H5–43 |
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ADAS |
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no |
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ADAS @ adas @ HNW1996 |
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489 |
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| Author |
Bartlomiej Twardowski; Pawel Zawistowski; Szymon Zaborowski |
| Title |
Metric Learning for Session-Based Recommendations |
Type |
Conference Article |
| Year |
2021 |
Publication |
43rd edition of the annual BCS-IRSG European Conference on Information Retrieval |
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12656 |
Issue |
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Pages |
650-665 |
| Keywords |
Session-based recommendations; Deep metric learning; Learning to rank |
| Abstract |
Session-based recommenders, used for making predictions out of users’ uninterrupted sequences of actions, are attractive for many applications. Here, for this task we propose using metric learning, where a common embedding space for sessions and items is created, and distance measures dissimilarity between the provided sequence of users’ events and the next action. We discuss and compare metric learning approaches to commonly used learning-to-rank methods, where some synergies exist. We propose a simple architecture for problem analysis and demonstrate that neither extensively big nor deep architectures are necessary in order to outperform existing methods. The experimental results against strong baselines on four datasets are provided with an ablation study. |
| Address |
Virtual; March 2021 |
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ECIR |
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LAMP; 600.120 |
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no |
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Admin @ si @ TZZ2021 |
Serial |
3586 |
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| Author |
Beata Megyesi; Alicia Fornes; Nils Kopal; Benedek Lang |
| Title |
Historical Cryptology |
Type |
Book Chapter |
| Year |
2024 |
Publication |
Learning and Experiencing Cryptography with CrypTool and SageMath |
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Historical cryptology studies (original) encrypted manuscripts, often handwritten sources, produced in our history. These historical sources can be found in archives, often hidden without any indexing and therefore hard to locate. Once found they need to be digitized and turned into a machine-readable text format before they can be deciphered with computational methods. The focus of historical cryptology is not primarily the development of sophisticated algorithms for decipherment, but rather the entire process of analysis of the encrypted source from collection and digitization to transcription and decryption. The process also includes the interpretation and contextualization of the message set in its historical context. There are many challenges on the way, such as mistakes made by the scribe, errors made by the transcriber, damaged pages, handwriting styles that are difficult to interpret, historical languages from various time periods, and hidden underlying language of the message. Ciphertexts vary greatly in terms of their code system and symbol sets used with more or less distinguishable symbols. Ciphertexts can be embedded in clearly written text, or shorter or longer sequences of cleartext can be embedded in the ciphertext. The ciphers used mostly in historical times are substitutions (simple, homophonic, or polyphonic), with or without nomenclatures, encoded as digits or symbol sequences, with or without spaces. So the circumstances are different from those in modern cryptography which focuses on methods (algorithms) and their strengths and assumes that the algorithm is applied correctly. For both historical and modern cryptology, attack vectors outside the algorithm are applied like implementation flaws and side-channel attacks. In this chapter, we give an introduction to the field of historical cryptology and present an overview of how researchers today process historical encrypted sources. |
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DAG |
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no |
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Admin @ si @ MFK2024 |
Serial |
4020 |
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| Author |
Beata Megyesi; Bernhard Esslinger; Alicia Fornes; Nils Kopal; Benedek Lang; George Lasry; Karl de Leeuw; Eva Pettersson; Arno Wacker; Michelle Waldispuhl |
| Title |
Decryption of historical manuscripts: the DECRYPT project |
Type |
Journal Article |
| Year |
2020 |
Publication |
Cryptologia |
Abbreviated Journal |
CRYPT |
| Volume |
44 |
Issue |
6 |
Pages |
545-559 |
| Keywords |
automatic decryption; cipher collection; historical cryptology; image transcription |
| Abstract |
Many historians and linguists are working individually and in an uncoordinated fashion on the identification and decryption of historical ciphers. This is a time-consuming process as they often work without access to automatic methods and processes that can accelerate the decipherment. At the same time, computer scientists and cryptologists are developing algorithms to decrypt various cipher types without having access to a large number of original ciphertexts. In this paper, we describe the DECRYPT project aiming at the creation of resources and tools for historical cryptology by bringing the expertise of various disciplines together for collecting data, exchanging methods for faster progress to transcribe, decrypt and contextualize historical encrypted manuscripts. We present our goals and work-in progress of a general approach for analyzing historical encrypted manuscripts using standardized methods and a new set of state-of-the-art tools. We release the data and tools as open-source hoping that all mentioned disciplines would benefit and contribute to the research infrastructure of historical cryptology. |
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DAG; 600.140; 600.121 |
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no |
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Admin @ si @ MEF2020 |
Serial |
3347 |
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| Author |
Benjia Zhou; Zhigang Chen; Albert Clapes; Jun Wan; Yanyan Liang; Sergio Escalera; Zhen Lei; Du Zhang |
| Title |
Gloss-free Sign Language Translation: Improving from Visual-Language Pretraining |
Type |
Conference Article |
| Year |
2023 |
Publication |
IEEE/CVF International Conference on Computer Vision (ICCV) Workshops |
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Sign Language Translation (SLT) is a challenging task due to its cross-domain nature, involving the translation of visual-gestural language to text. Many previous methods employ an intermediate representation, i.e., gloss sequences, to facilitate SLT, thus transforming it into a two-stage task of sign language recognition (SLR) followed by sign language translation (SLT). However, the scarcity of gloss-annotated sign language data, combined with the information bottleneck in the mid-level gloss representation, has hindered the further development of the SLT task. To address this challenge, we propose a novel Gloss-Free SLT based on Visual-Language Pretraining (GFSLT-VLP), which improves SLT by inheriting language-oriented prior knowledge from pre-trained models, without any gloss annotation assistance. Our approach involves two stages: (i) integrating Contrastive Language-Image Pre-training (CLIP) with masked self-supervised learning to create pre-tasks that bridge the semantic gap between visual and textual representations and restore masked sentences, and (ii) constructing an end-to-end architecture with an encoder-decoder-like structure that inherits the parameters of the pre-trained Visual Encoder and Text Decoder from the first stage. The seamless combination of these novel designs forms a robust sign language representation and significantly improves gloss-free sign language translation. In particular, we have achieved unprecedented improvements in terms of BLEU-4 score on the PHOENIX14T dataset (>+5) and the CSL-Daily dataset (>+3) compared to state-of-the-art gloss-free SLT methods. Furthermore, our approach also achieves competitive results on the PHOENIX14T dataset when compared with most of the gloss-based methods. |
| Address |
Vancouver; Canada; June 2023 |
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ICCVW |
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HUPBA; |
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no |
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Admin @ si @ ZCC2023 |
Serial |
3839 |
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| Author |
Bhalaji Nagarajan; Marc Bolaños; Eduardo Aguilar; Petia Radeva |
| Title |
Deep ensemble-based hard sample mining for food recognition |
Type |
Journal Article |
| Year |
2023 |
Publication |
Journal of Visual Communication and Image Representation |
Abbreviated Journal |
JVCIR |
| Volume |
95 |
Issue |
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Pages |
103905 |
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| Abstract |
Deep neural networks represent a compelling technique to tackle complex real-world problems, but are over-parameterized and often suffer from over- or under-confident estimates. Deep ensembles have shown better parameter estimations and often provide reliable uncertainty estimates that contribute to the robustness of the results. In this work, we propose a new metric to identify samples that are hard to classify. Our metric is defined as coincidence score for deep ensembles which measures the agreement of its individual models. The main hypothesis we rely on is that deep learning algorithms learn the low-loss samples better compared to large-loss samples. In order to compensate for this, we use controlled over-sampling on the identified ”hard” samples using proper data augmentation schemes to enable the models to learn those samples better. We validate the proposed metric using two public food datasets on different backbone architectures and show the improvements compared to the conventional deep neural network training using different performance metrics. |
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MILAB |
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no |
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Admin @ si @ NBA2023 |
Serial |
3844 |
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| Author |
Bhalaji Nagarajan; Ricardo Marques; Marcos Mejia; Petia Radeva |
| Title |
Class-conditional Importance Weighting for Deep Learning with Noisy Labels |
Type |
Conference Article |
| Year |
2022 |
Publication |
17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications |
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5 |
Issue |
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Pages |
679-686 |
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Noisy Labeling; Loss Correction; Class-conditional Importance Weighting; Learning with Noisy Labels |
| Abstract |
Large-scale accurate labels are very important to the Deep Neural Networks to train them and assure high performance. However, it is very expensive to create a clean dataset since usually it relies on human interaction. To this purpose, the labelling process is made cheap with a trade-off of having noisy labels. Learning with Noisy Labels is an active area of research being at the same time very challenging. The recent advances in Self-supervised learning and robust loss functions have helped in advancing noisy label research. In this paper, we propose a loss correction method that relies on dynamic weights computed based on the model training. We extend the existing Contrast to Divide algorithm coupled with DivideMix using a new class-conditional weighted scheme. We validate the method using the standard noise experiments and achieved encouraging results. |
| Address |
Virtual; February 2022 |
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VISAPP |
| Notes |
MILAB; no menciona |
Approved |
no |
| Call Number |
Admin @ si @ NMM2022 |
Serial |
3798 |
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| Author |
Bhaskar Chakraborty |
| Title |
Model free approach to human action recognition |
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Book Whole |
| Year |
2012 |
Publication |
PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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Automatic understanding of human activity and action is very important and challenging research area of Computer Vision with wide applications in video surveillance, motion analysis, virtual reality interfaces, video indexing, content based video retrieval, HCI and health care. This thesis presents a series of techniques to solve the problem of human action recognition in video. First approach towards this goal is based on a probabilistic optimization model of body parts using Hidden Markov Model. This strong model based approach is able to distinguish between similar actions by only considering the body parts having major contributions to the actions. In next approach, we apply a weak model based human detector and actions are represented by Bag-of-key poses model to capture the human pose changes during the actions. To tackle the problem of human action recognition in complex scenes, a selective spatio-temporal interest point (STIP) detector is proposed by using a mechanism similar to that of the non-classical receptive field inhibition that is exhibited by most oriented selective neuron in the primary visual cortex. An extension of the selective STIP detector is applied to multi-view action recognition system by introducing a novel 4D STIPs (3D space + time). Finally, we use our STIP detector on large scale continuous visual event recognition problem and propose a novel generalized max-margin Hough transformation framework for activity detection |
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Ph.D. thesis |
| Publisher |
Ediciones Graficas Rey |
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Jordi Gonzalez;Xavier Roca |
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ISE |
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no |
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Admin @ si @ Cha2012 |
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2207 |
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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 |
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Barcelona, Spain |
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ISE |
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no |
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Admin @ si @ Cha2008 |
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1149 |
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| Author |
Bhaskar Chakraborty; Andrew Bagdanov; Jordi Gonzalez |
| Title |
Towards Real-Time Human Action Recognition |
Type |
Conference Article |
| Year |
2009 |
Publication |
4th Iberian Conference on Pattern Recognition and Image Analysis |
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5524 |
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This work presents a novel approach to human detection based action-recognition in real-time. To realize this goal our method first detects humans in different poses using a correlation-based approach. Recognition of actions is done afterward based on the change of the angular values subtended by various body parts. Real-time human detection and action recognition are very challenging, and most state-of-the-art approaches employ complex feature extraction and classification techniques, which ultimately becomes a handicap for real-time recognition. Our correlation-based method, on the other hand, is computationally efficient and uses very simple gradient-based features. For action recognition angular features of body parts are extracted using a skeleton technique. Results for action recognition are comparable with the present state-of-the-art. |
| Address |
Póvoa de Varzim, Portugal |
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Springer Berlin Heidelberg |
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LNCS |
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0302-9743 |
ISBN |
978-3-642-02171-8 |
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IbPRIA |
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ISE |
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no |
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DAG @ dag @ CBG2009 |
Serial |
1215 |
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| Author |
Bhaskar Chakraborty; Andrew Bagdanov; Jordi Gonzalez; Xavier Roca |
| Title |
Human Action Recognition Using an Ensemble of Body-Part Detectors |
Type |
Journal Article |
| Year |
2013 |
Publication |
Expert Systems |
Abbreviated Journal |
EXSY |
| Volume |
30 |
Issue |
2 |
Pages |
101-114 |
| Keywords |
Human action recognition;body-part detection;hidden Markov model |
| Abstract |
This paper describes an approach to human action recognition based on a probabilistic optimization model of body parts using hidden Markov model (HMM). Our method is able to distinguish between similar actions by only considering the body parts having major contribution to the actions, for example, legs for walking, jogging and running; arms for boxing, waving and clapping. We apply HMMs to model the stochastic movement of the body parts for action recognition. The HMM construction uses an ensemble of body-part detectors, followed by grouping of part detections, to perform human identification. Three example-based body-part detectors are trained to detect three components of the human body: the head, legs and arms. These detectors cope with viewpoint changes and self-occlusions through the use of ten sub-classifiers that detect body parts over a specific range of viewpoints. Each sub-classifier is a support vector machine trained on features selected for the discriminative power for each particular part/viewpoint combination. Grouping of these detections is performed using a simple geometric constraint model that yields a viewpoint-invariant human detector. We test our approach on three publicly available action datasets: the KTH dataset, Weizmann dataset and HumanEva dataset. Our results illustrate that with a simple and compact representation we can achieve robust recognition of human actions comparable to the most complex, state-of-the-art methods. |
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| Series Editor |
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Series Title |
|
Abbreviated Series Title |
|
| Series Volume |
|
Series Issue |
|
Edition |
|
| ISSN |
|
ISBN |
|
Medium |
|
| Area |
|
Expedition |
|
Conference |
|
| Notes |
ISE |
Approved |
no |
| Call Number |
Admin @ si @ CBG2013 |
Serial |
1809 |
| Permanent link to this record |
